perform the classification comprised agriculture, bare land, building area, eucalyptus, maritime pine, mixed forest (which includes riparian and non-riparian), and shrubland. Additionally, the classified data from 2017 was utilized to classify fuel types and hazards (Table 2). According to the Portuguese custom fuel model(Fernandes et al., 2006; Fernandes & Loureiro, 2021), fuel hazard values range from 1 (very low) to 5 (very high). Vegetated areas were classified on a scale of 2 to 5 because the fuel model runs from 1 to 4. To encompass additional land cover types in the classification, such as water, barren land, houses, and agriculture, were assigned 1 because they are not considered wildfire fuel.

<table><caption>Table 2: Sentinel-2 images collected for 2017 and 2018.</caption><thead><tr><th>Fuel Types – LULC classes</th><th>Fuel Models</th><th>Fire Hazard</th></tr></thead><tbody><tr><td>Building; Water; Agriculture; Bare</td><td>1</td><td>Very Low</td></tr><tr><td>Mixed Forest – Riparian and Non-Riparian</td><td>2</td><td>Low</td></tr><tr><td>Maritime Pine Stands</td><td>3</td><td>Moderate</td></tr><tr><td>Shrubland – Small Vegetation</td><td>4</td><td>High</td></tr><tr><td>Eucalyptus Stands</td><td>5</td><td>Very High</td></tr></tbody></table>

The VIs were estimated using the ArcGIS Pro® indices tool, using pre-fire satellite images. In this case, only the largest forest fires that occurred in 2017, designated as Burned Area 1 (BA1), which occurred in September and Burned Area 2 (BA2), which occurred in October (Table 1), were considered in this analysis. Since both events are classified as extreme wildfires because they covered more than 100 hectares, BA1 has 5,433 hectares, while BA2 has 666,3 hectares.

First, the NDVI was computed (1). Its values vary from -1 to 1. Values near 0 represent bare, rocks, sand, and snow. Low positive scores (0.2-0.4) are associated with shrubs and grassland. Values greater than 0.4 imply live green vegetation (Rouse et al., 1973).

$$NDVI = \frac{(Band 8 - Band 3)}{(Band 8 + Band 3)} \quad (1)$$

The NDII (2) is sensitive to variations in the water content of the plant canopy (Hardisky et al., 1983). The value rises as water content rises. NDII

measurements can range from -1 to 1, with green vegetation often falling between 0.02 and 0.6.

$$NDII = \frac{(Band 8 - Band 11)}{(Band 8 + Band 11)} \quad (2)$$

The PSRI (3) assesses plant senescence (Merzlyak et al., 1999). PSRI values, like NDVI, range from -1 to 1. Although values ranging from -0.1 to 0.2 suggest healthy vegetation, values greater than 0.2 imply senescence and values less than -0.1 are associated with other landscape characteristics such as water and buildings.

$$PSRI = \frac{(Band 2 - Band 4)}{(Band 6)} \quad (3)$$

While higher NDVI values suggest greater greenness, PSRI indicates significant senescence. To measure fuel availability, a composite using ArcGIS Pro® composite band tool, of the three VIs was used. To use PSRI in conjunction with NDVI and NDII, it was multiplied by -1. Converting positive senescence numbers to negative values. The compositions were then sliced based on wildfire fuel availability, which ranged from 1 to 5, based on what was reported by Bento-Gonçalves et al., (2019a). Where 1 represents very low, 2 represents low, 3 represents moderate, 4 represents high, and 5 represents extremely high wildfire fuel source.

Vector data containing polygons from burned regions BA1 and BA2 were obtained from the Portuguese National Authority for Nature Conservation (ICNF) and compared to VIs and wildfire fuels data. This was conducted to see if the spatial information generated corresponds to the perimeter of the burned regions in official data. The classified image from 2017 was then compared to 2018 data to determine the percentage change following the occurrence of these extreme events.

## 3 RESULTS AND DISCUSSION

VIs were estimated for the two greatest burned areas (BA1 and BA2) in Vale do Sousa before wildfire occurrence. In terms of vegetation conditions for BA1, the NDVI values (figure 2) range from -0.06 to 0.60, with a mean of 0.16 and a standard deviation of ±0.11. It is considered healthy vegetation when NDVI values are higher than 0.40, and the average value likely implies that the area is probably primarily covered by shrubland. This class covers roughly 318,84 ha, accounting for 48% of the total area.
<figure><img src="image_35.png" /><figcaption>Figure 2: NDVI of BA1.</figcaption></figure>

However, the area contains Eucalyptus, Maritime Pine, and Mixed Forest stands, which is why the NDVI maximum value is 0.60.

The humidity index, NDII (figure 3), ranges from 0.41 to 0.38, with a mean of -0.07 and a standard deviation of ±0.09. As can be observed, moisture content in September had a negative mean, and the lowest values are below what is considered healthy green vegetation (between 0.02 and 0.6), showing that the vegetation of area BA1 was suffering from water stress.

<figure><img src="image_36.png" /><figcaption>Figure 3: NDII of BA1.</figcaption></figure>

Regarding PSRI (figure 4), which was multiplied by -1 as described in Bento-Gonçalves et al., (2019a),

negative values now signify senescence, and positive values represent healthy vegetation. This was done to create a composition that would evaluate wildfire fuel availability for both areas of interest. PSRI scores vary from -0.51 to 0.06, with an average of -0.08 and a standard deviation of ±0.034.

<figure><img src="image_37.png" /><figcaption>Figure 4: PSRI of BA1.</figcaption></figure>

This indicates that the majority of the vegetation in region BA1 falls between the values considered as healthy green vegetation. While a minor portion exceeds the threshold for healthy vegetation, ranging from -0.21 to -0.51.

<figure><img src="image_38.png" /><figcaption>Figure 5: NDVI of BA2.</figcaption></figure>
Area BA2 is substantially larger than BA1, covering around 5,433 hectares. The NDVI (Figure 5) suggests that the vegetation is photosynthetically active, as the values range from -0.21 to 0.88, with a mean of 0.68 and a standard deviation of ±0.12. The forested impacted area is mostly covered by Eucalyptus, followed by Maritime Pine stands, and small regions of mixed forest. The high average value is most likely owing to the region's substantial forested land.

<figure><img src="image_39.png" /><figcaption>Figure 6: NDII of BA2.</figcaption></figure>

The NDII (figure 6) minimum value is -0.51, and the maximum value is 0.62, with a mean of 0.22 and a standard deviation of ±0.14. This indicates that most of the vegetation's humidity was closer to the threshold of what is considered water stress than healthy vegetation. Furthermore, a wildfire occurred in October of 2017, when the weather was dry and hot, which is unusual for this month (IPMA, 2017). All of these elements most likely contributed to the escalation of these events, which had ramifications for Vale do Sousa's social, economic, and environmental conditions.

Regarding the VI senescence (figure 7), the PSRI's minimum value is -0.93, indicating extreme senescence in some portion of the wooded area, the highest value, which indicates healthy vegetation of 0.40. Although the negative mean value of -0.12 with a standard deviation of ±0.06, is within what is considered healthy vegetation, demonstrates the effect of the high negative values on the mean, changing the distribution of the data.

With this information, it was possible to compute the fuel availability for both areas (Bento-Gonçalves

et al., 2019a). As shown in Figure 7, the fuel ranges from very low to moderate in BA1 and very low to very high in BA2. Nonetheless, only classes above 2 were considered wildfire fuel, implying that, while both areas are primarily covered by class 1, there are small patches of class 2 and 3 in BA1, and areas classified from 2 to 5 in BA2, indicating that fuel was available in both areas.

<figure><img src="image_40.png" /><figcaption>Figure 7: PSRI of BA2.</figcaption></figure>

The ML method used to perform the classification of the selected scenes of 2017 and 2018 was Support Vector Machine, followed by the computation of a confusion matrix and accuracy assessment. The classification was evaluated using the Kappa Index, which is greater than 0,8, indicating high accuracy of the classification. Image from 2017 was classified to identify the fuel type (Fernandes et al., 2006) and, as a result, fuel hazards maps were created for both areas, is also shown in Figure 7.

Aside from water stress, senescence, and vegetation health, wildfires are also affected by climate, slope and fuel hazards. Because fuel hazard influences fire spread and magnitude. As can be seen, a large section of BA1 is classified as 4 (High), with 214 ha classified as 5 (very high). Demonstrating how forest cover types can have an important influence on these events. It is worth mentioning that for this study, the vegetation structure was not evaluated. Classes 3-5 are related to Maritime Pine, shrubland, and Eucalyptus. Maritime Pine was classified as 3 (moderate) because it has fewer stands than Eucalyptus. Shrub was classed as 4 (High) because the area with this classification is quite close to the
<figure><img src="image_41.png" /><figcaption>Figure 8: Wildfire fuel and Fuel hazard maps of 2017.</figcaption></figure>

above-listed tree species' stands. According to (Fernandes et al., 2006), the subcover can affect the spread and fire hazard. Eucalyptus covers a larger area in BA2, accounting for 52% of the entire area. Maritime pine represents 23.4% of the total area. When looking at VIs, it is easy to see lower or extreme values of the forest's biophysical state. The significant fuel availability and hazards in the study area most likely contributed to the large fire. From 2017 to 2018, the change in landcover type of BA1 reveals a 35% decline in Eucalyptus and 52.5% in

maritime pine stands. These percentages reflect the conversion of those categories to bare and shrubland cover types in 2018.

There were no eucalyptus losses in the case of BA2, but the same did not occur to maritime pine stands. According to the changes observed between 2017 and 2018, the eucalyptus area had an increase of 1%. Whereas maritime pine lost nearly 90% of its area. However, this loss may be due not only to wildfire effects but also to harvesting, as Eucalyptus plantations have increased in recent years in the study
area. It is worth noting that bare land and shrubland cover types have also increased. They accounted for barely 3% in 2017, but almost 30% in 2018.

As illustrated, all of these factors may influence extreme wildfire outbreaks. Although fuel availability with extreme value only accounts for a small percentage of the region of interest, when combined with VIs and fuel hazard score, it revealed that those locations were prone to wildfires before they occurred, both PSRI and fuel hazard classes had higher values. When these remote sensing products are combined, they can be valuable in pre-fire management actions by identifying where precautions should be taken to reduce losses caused by wildfires, as shown in the change analysis.

One weakness of this study is the classification of fuel hazards. Because forest structure was not included in this study. In this manner, the Portuguese fuel model was modified to be used with the classified image. Classes were clustered, which could affect hazardous classification, as the structure becomes more important to fire behavior than tree species (Fernandes et al., 2006). It is critical to determine the structural types of the stands (closed, low, or tall; open, low, or tall) if the objective is to develop a fuel hazard model alongside VIs and fuel availability.

Despite this limitation, the primary goal of this study was to identify available remote sensing-derived products as support tools for fuel monitoring and management throughout the wildfire season. Since both wildfires occurred in areas where VIs indicated great senescence, low water content and less greenness, fuel availability could be identified in the study region. With fuel availability classified as low to medium in BA1, and low to very high in BA2.

The modified fuel hazards model for both locations identified vast areas classified as high or extremely high. It was possible to demonstrate that the remote sensing-derived data information is reliable in detecting alterations in forest biophysical properties. Furthermore, this information could potentially be employed as an auxiliary tool or as information when carrying out the most effective fire control measures in order to manage fuel loading while decreasing fire severity and fulfil forest fire management objectives.

## 4 CONCLUSIONS

Monitoring forest fire fuels is critical for implementing best practices management approaches to mitigate wildfire threats to forest ecosystems, population health, and financial losses. With the

availability of a variety of satellite constellations, such as Sentinel-2, VIs may be used as additional tools to monitor fuel loading and hazards, hence reducing the severity of wildfires.

Data is available and accessible across a variety of platforms, and VIs resulting from remote sensing imagery can be computed through different GIS tools. Furthermore, decision-makers could employ these spatial data to support forest fire prevention activities.

Despite its limitations, this approach remains a significant resource, particularly in locations where fieldwork is not feasible owing to financial or logistical constraints. The attempts to reduce wildfire fuel could be improved by looking at VIs along with fuel hazard models and other management actions, suggesting which regions need to be investigated when selecting where the efforts to reduce fuels should be invested.

Further studies should be performed to verify additional burned areas from previous years utilizing alternative satellite constellations such as Landsat. In addition, other VIs should be explored to verify the forest's biophysical composition. Although LiDAR data was not used in this study, it is still an important remote sensing data as it may provide information about the structure of the forest in less time and assess understory fuels.

## ACKNOWLEDGEMENTS

Bárbara Pavani-Biju was financially supported by Portuguese national funds through the Foundation for Science and Technology I.P. (grant reference number: 2022.13033.BD), Decision Support for the Supply of Ecosystem Services under Global Change (DecisionES) (grant agreement number: 101007950-H2020-MSCA-RISE-2020), and through FCT – Fundação para a Ciência e Tecnologia, I.P., in the framework of the ICT project with the references UIDB/04683/2020 and UIDP/04683/2020.

José G. Borges and Susete Marques were financially supported by Portuguese national funds through the Foundation for Science and Technology I.P. (project references UIDB/00239/2020 and UIDP/00239/2020 of the Forest Research Centre and DOI identifier 10.54499/UIDB/00239/2020 and 10.54499/UIDP/00239/2020 and the Associate Laboratory Terra (TERRA LA/P/0092/2020. Susete Marques was also funded by national funds through the FCT, in the scope of Norma Transitória – DL57/2016/CP13827CT15.
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# Modelling the Road Network as an Expression of Historical Spatial System Change in the Klaipeda Region (Lithuania)

Thomas Gloaguen¹,² Dª, Kęstutis Zaleckis¹ Dᵇ and Sébastien Gadal² Dᶜ

¹Cultural and Spatial Environment Research Group, Faculty of Civil Engineering and Architecture, Kaunas University of Technology, 51367 Kaunas, Lithuania

²UMR 7300 ESPACE, CNRS, Aix-Marseille Université, Université Côte-d'Azur, Avignon Université, 84029 Avignon, France

thomas.gloaguen@ktu.edu, kestutis.zaleckis@ktu.lt, sebastien.gadal@univ-amu.fr

**Keywords:** Spatial System, Network Structures, Generic City Concept, Space Syntax, Spatiotemporal Modelling, Lithuania.

**Abstract:** Since the 20th century, the Klaipėda region (Lithuania) has undergone significant political, economic, social, and cultural changes related to the disappearance of Prussia (1923), the Soviet occupation (1944) and the restoration of independence (1991). This has led to radical transformations in the spatial system, of which the road network is a key component. Networks could be analysed using space syntax methods by assimilating them to a mathematical graph model, for which quantitative indexes based on topology can be applied to assess their spatial configurations. The 'Generic City' approach, by simulating foreground and background urban networks, can be modified innovatively on a territorial scale. Based on historical cartographies and open geographic databases, indexes characterising both networks are derived to describe the length, angularity or accessibility of the pre-Soviet, Soviet and post-Soviet networks. The analysis identified four main spatial structures associated with the network by combining the indexes in a K-means machine learning algorithm. They highlight the spatial impacts of collectivisation, industrialisation, and tertiarisation of the economy, post-World Wars and post-Cold War geopolitical events, and their consequences as drivers of the territorial organisation and dynamics such as the metropolisation or peri-urbanisation.

## 1 INTRODUCTION

Since the 20th century, the coastal region of Klaipėda in Lithuania has undergone significant and rapid transformations. These changes were initially political, with the territory being divided between the Kingdom of Prussia and the Russian Empire, experiencing a brief period of independence during the interwar years, being incorporated into the Union of Soviet Socialist Republics (USSR) in 1944, and finally seeing the restoration of independence in 1991 (Plakans, 2011). These geopolitical shifts have been accompanied by economic, social, cultural, and societal changes, including agrarian reforms such as agrarian capitalism and collectivisation (Jespen et al., 2015), urban expansion, particularly suburban sprawl (Cirtautas, 2015), Soviet industrialisation, and the

subsequent deindustrialisation and tertiarisation after the Soviet era (Idzelis, 1984; Köll, 2024), alongside recent trends of population decline and migration (Ubarevičienė and Van Ham, 2017), throughout the 20th and early 21st centuries.

The organisation of geographic space is significantly influenced by societal changes, whether directly or indirectly. The analysis of these territorial transformations is rooted in the concept of spatial systems, which refers to the distribution (structures) of geographical entities — such as built environments, road networks, land use, land cover, and land parcels — in space according to either spontaneous or regulated logics (Archaeomedes, 1998; Allain, 2004; Ellisalde, 2004).

The objective of this research is to examine the evolution of the spatial system in the Klaipėda region

<footnote>ª https://orcid.org/0000-0003-1849-9615</footnote>

<footnote>ᵇ https://orcid.org/0000-0001-9223-9956</footnote>

<footnote>ᶜ https://orcid.org/0000-0002-6472-9955</footnote>
(Lithuania) since the 20th century. To achieve this objective, the road network has been modelled using space syntax methods, with a particular focus on the 'Generic City' concept.

The analysis of spatial systems involves examining their structural complexity, which includes the participation of multiple agents; spatial complexity characterised by various geographical scales; and temporal complexity marked by non-linear evolutions (Badariotti, 2005). Spatial analysis and modelling offer methodologies for comprehending the intricacies of spatial systems by translating geographical reality into mathematical forms (Georges and Verger, 2013). This methodological approach has been employed to describe "[...] the distribution, configuration and covariation" of geographical objects in space" (Voiron-Canicio, 1995).

In complex spatial systems, networks facilitate the relationships between various points of attraction and diffusion, such as urban centres (Georges and Verger, 2013). The mathematical graph model serves as the theoretical basis for space syntax, wherein networks are represented by nodes corresponding to the network segments and links connecting these nodes. This theoretical framework enables the simulation of interrelationships between spatial configurations and their associated urban functions (Laouar and Manzouz, 2017; Abida, 2018). Syntactic indexes based on the topology of network segments are utilised to examine how spatial structure influences human behaviour through simulated movement.

This research focuses on the Klaipėda region, where spatial changes are particularly pronounced compared to the rest of Lithuania. The unique environment of this coastal area is attributable to its position as a maritime interface. During the Soviet era, the port of Klaipėda emerged as a pivotal facilitator of connectivity between the Baltic and the Black Sea regions, driving the development of strategic economic activities alongside the growth of seaside tourism. Despite the region experiencing partial militarisation during this period, Lithuania's independence in the 1990s led to the enhancement of these activities and an opening to the market economy. A consequence of this transformation has been the rapid suburbanisation of the metropolitan and coastal areas that began in the 2000s (Vaitkus and Vaikuvienė, 2005; Veteikis et al., 2011; Cirtautas, 2015). Furthermore, the region is historically divided between two ethnographic areas: Samogitia (Žemaitija) in the north and Lithuania Minor (Mažoji Lietuva) in the south, each exhibiting distinct functional and cultural characteristics (Ragauskaitė,

2019). The former corresponds to the territory that was under Russian occupation, while the latter was incorporated into Prussia prior to the Soviet era.

## 2 THE 'GENERIC CITY' CONCEPTUAL APPROACH

This analysis draws upon the concept of the 'Generic City', developed within space syntax theories and methodologies to identify the spatial and functional similarities between cities despite their diverse cultural contexts (Hillier, 2007; Hillier, 2010).

This concept proposes that each city exhibit a dual network characterised by (1) "longer lines and nearly straight connections", indicating a foreground network, and (2) "shorter lines with more near right angle connections, and so more localised and with less linear continuity", referring to a background network.

The foreground network tends to be similar across various cities, while the background network is more specific and sensitive to cultural context. These networks, with their distinct physical and functional attributes, are evaluated using specific indexes (section 3.2).

For instance, Al Sayed (2009) compared the foreground and background networks of Manhattan and Barcelona to showcase the emergence of dynamic and adaptive urban networks. In the present study, however, the indexes traditionally used to characterise these networks are applied not in an urban context but in a territorial one. They prove to be insufficiently suited to the research objectives due to their low statistical reliability and limited differentiation between foreground and background networks. The diversity of urban and rural settlements (including individual and familial farms, villages, towns, and cities) in relation to road networks presents varying degrees of appeal for movements across the territory (Dringelis, 2015). Furthermore, the region has been shaped by multiple cultural influences — Prussian, Russian, Soviet, and post-Soviet — which have successively affected territorial organisation, complicating the modelling of these networks. Consequently, it has become essential to employ syntactic indexes that more accurately reflect the conceptual approach of the Generic City in a territorial context (Gloaguen et al., 2023).
# 3 SYNTACTIC TERRITORIAL INDEXES FOR SPATIOTEMPORAL MODELLING OF NETWORK STRUCTURES

## 3.1 Data Acquisition

The present analysis adopts a historical retrospective approach, necessitating the identification of representative analytical periods that reflect differing political, economic, social, and cultural contexts. According to the scientific literature and the conceptualisations proposed by some Lithuanian researchers regarding the urban structures of Baltic cities (Cirtautas, 2013) and rural settlements and landscapes (Bučas, 2001), three primary recurring periods are typically used for analysing the 20th and 21st centuries: the pre-Soviet (before 1945), the Soviet (1945-1990), and the post-Soviet (after 1990).

The selection of these periods is also influenced by the availability of cartographic data. Historical cartographic resources are utilised for both pre-Soviet and Soviet periods. Piškinaitė and Veteikis (2023) note that cartographic materials are still underutilised by researchers in Lithuania. For the initial period, the series ‘Karte des Deutschen Reiches’ (Map of the German Reich, 1870-1944) and ‘Karte des Westlichen Russlands’ (Map of Western Russia, 1892-1921) are employed, both at a resolution of 1:100,000. For the subsequent period, Soviet military topographic maps from 1985 at a resolution of 1:50,000 are used. The choice of these series is predicated on several criteria, including their free availability, the quality of their scanning, and the inclusion of a legend for analysis and digitisation purposes.

For the latest recent period (2023), free geographic data from OpenStreetMap (https://www.openstreetmap.org/) was extracted using the ‘Over Pass Turbo’ tool (https://overpass-turbo.eu/). Barrington-Leigh and Millard-Ball (2017) estimate the completeness fraction of the OpenStreetMap road network in Lithuania is approximately 97% reflecting the accuracy of the information compared to actual conditions.

The interactive, manually digitised data was then processed to exclude unwanted networks, such as footpaths, cycle paths, and occasionally utilised roads like forest tracks. This process also involved the removal of duplicate and isolated lines, as well as a check for their connectivity.

## 3.2 ‘Generic City’ Indexes Adapted to Territorial Analysis

The indexes are derived from the theoretical framework that treats the network as a mathematical graph, where nodes represent network segments and links signify the connections between them. Quantitative methodologies that prioritise the topology of these segments are employed to assess various spatial configurations, including centrality, connectivity, accessibility, and movement.

Four indexes from the Spatial Network Analysis Software (sDNA) library (https://sdna.cardiff.ac.uk/sdna/) are utilised to characterise the foreground and background networks (Cooper and Chiaradia, 2020; Cooper, 2024). These indexes were initially chosen based on experiments conducted in the same study area, where their correlation with human activity data — such as population density, amenities, and light pollution — was assessed. The observed correlation coefficients, which range from 0.61 to 0.74 on average, reflect strong to extraordinarily strong relationships, making them relevant for the analysis (Gloaguen et al., 2022).

For the background network indexes, a radius of 1000 metres is applied to simulate local network behaviour.

The Network Quantity Penalized by Distance (NQPD) index is computed for both foreground and background networks. This index quantifies the number of accessible segments (network quantity) within a specified radius of a given segment (equation 1). Each segment is regarded as equally attractive, thereby allowing the focus to be solely on distance.

$$NQPD(x) = \sum_{y \in R_x} \frac{(W(y)P(y))^{nqpdn}}{d_M(x, y)^{nqpdd}} \quad (1)$$

Where,

$R_x$ is the set of polylines in the network radius from the link $x$.

$W(y)$ is the weight of a polyline $y$.

$P(y)$ is the proportion of any polyline $y$ within the radius.

$d_M(x, y)$ is the distance according to a metric $M$ along a geodesic defined by $M$, between an origin polyline $x$ and a destination polyline $y$. $nqpdn$ and $nqpdd$ are the numerator and denominator of NQPD, usually defined as 1.

The NQPD index emphasises the most accessible areas of the network, which often align with the urban central zone, characterised by high multifunctionality.
Three indexes are exclusively used to describe the background network. First, the ‘Length’ index (LEN) quantifies the cumulative length of segments within a specified radius (equation 2).

$$LEN = \sum_{y \in R_x} L(y)P(y) \quad (2)$$

Where,

$R_x$ is the set of polylines in the network radius from the link $x$.

$L(y)$ is the Euclidean length of polyline $y$.

$P(y)$ is the proportion of any polyline $y$ within the radius.

This index can be likened to Peponis’s (2008) ‘Metric Reach’ index, as it underscores the physical extent of connections and assesses the utilisation of network areas.

Second, the ‘Angular Distance’ index (ANG) captures the total angular curvature of segments within the radius (equation 3).

$$ANG = \sum_{y \in R_x} d_{\theta}(y_R) \quad (3)$$

Where,

$R_x$ is the set of polylines in the network radius from the link $x$.

$d_{\theta}$ is the cumulative angular curvature along the full length of the line in degrees.

$y_R$ is the proportion of $y$ that falls within the radius only.

Unlike the Length index, this index focuses solely on closeness (geometrical proximity) and does not account density.

Finally, the ‘Betweenness’ index (BT) (equation 4) assesses the frequency with which a segment appears in the shortest paths connecting other segments within a designated radius.

$$BT(x) = \sum_{y \in N} \sum_{z \in R_y} W(y)W(z)P(z)OD(y, z, x) \quad (4)$$

Where,

$N$ is the set of polylines in the global spatial system.

$R_y$ is the set of polylines in the network radius from the link $y$.

$W(y)$ and $W(z)$ are the weight of the geodesic endpoints $y$ and $z$.

$P(z)$ is the proportion of any polyline $z$ within the radius.

$OD(y, z, x)$ are the geodesic paths between $y$ and $z$ that pass through a vertex $x$.

The Betweenness index reflects transit movements, highlighting the key segments of the network.

All indexes are standardised by dividing their value by the standard deviation, which enables comparisons across networks of varying sizes. Standardisation also accommodates the spatial resolutions of the geographic data utilised over different periods.

## 3.3 Machine Learning Modelling of the Spatial Structures of the Network

To identify the principal spatial structures associated with the networks and their evolution over time, the foreground and background indexes are integrated for multivariate analysis.

The networks are overlaid on a 1km-by-1km grid, and the average values of each index are calculated. The grid size corresponds to the radius value used for the background network indexes. However, it is crucial to note that the selected grid size influences the interpretation of results, as does the number of clusters.

A multivariate analysis was conducted using a K-means algorithm to minimise the variance of values grouped into clusters. Effectively generalise and visualise the results of the indexes, the number of clusters was established at four. Each cluster represents a spatial structure of the road network that corresponds to the territorial organisation, comparable both in time and space.

# 4 EVOLUTIONS OF FOREGROUND AND BACKGROUND NETWORKS

The foreground network has experienced considerable evolution over time. As illustrated in Figure 1, the centre of gravity of the network is gradually shifting from the south during the pre-Soviet period to the centre of the coastal region in the post-Soviet period. The NQPD index indicates a decline in regional accessibility of the network over time, with average standardised values of 6.19 for the pre-Soviet network, 5.24 for the Soviet network, and 4.22 for the post-Soviet network.

At the local level, two trends emerge: a decrease in the NQPD index from the pre-Soviet (1.06) to Soviet (0.94) periods, followed by an increase between the Soviet and post-Soviet periods (1.20) (Figure 2). These findings suggest a densification and
<figure><img src="image_42.png" /><figcaption>Figure 1: Spatial evolution of the foreground network between the (a) pre-Soviet, (b) Soviet, and (c) post-Soviet periods.</figcaption></figure>

local expansion of the network, a trend also noted by Strano et al. (2012) in their analysis of network evolution in the suburbs of Milan. Notably, the suburbs of Klaipėda witnessed significant densification between the Soviet and post-Soviet periods.

The Length and Angular Distance indexes, which measure the potential for social interactions and the organicity of the local networks, respectively, have shown a decrease in their standardised values over time. The average value for Length in the post-Soviet period is 1.64, compared to 1.80 for the Soviet period and 1.93 for the pre-Soviet period (Figure 2). Regarding Angular Distance, the average value for the post-Soviet period stands at 1.08, in contrast to 1.17 for the Soviet period and 1.21 for the pre-Soviet period (Figure 2).

<figure><img src="image_43.png" /><figcaption>Figure 2: Quantitative evolution of background network indexes.</figcaption></figure>

The betweenness index, which highlights the significance of a segment within the local network, shows an increase in the average standardised values from the pre-Soviet (0.33) to the Soviet period (0.49), followed by a stabilisation between the Soviet and post-Soviet periods (0.47) (Figure 2). This increase in betweenness values suggests that the hierarchy among routes is becoming more clearly defined over time at the local level.

# 5 HIERARCHY AND DYNAMICS OF NETWORK STRUCTURES

By integrating the foreground network and background network indexes into a multivariate analysis, the primary spatial structures of the Klaipėda region's spatial system are identified. These structures are characterised by (1) the key local centralities, (2) the territorial centre of gravity, (3) secondary centralities in transition and (4) peripheral areas. Their respective evolution is interpreted in relation to existing scientific literature.

## 5.1 Main Local Centres

The first spatial structure is linked to grid tiles that possess the highest average indexes, irrespective of the type of network (Figure 3).

These grid tiles represent the main local centralities within the network. Between the pre-Soviet and post-Soviet periods, the networks associated with the region's primary centres underwent a complete reconfiguration. In the pre-Soviet era, the main local centres were situated in the
<figure><img src="image_44.png" /><figcaption>Figure 3: Spatial evolution of networks relating to local centres between the (a) pre-Soviet, (b) Soviet, and (c) post-Soviet periods.</figcaption></figure>

with noteworthy development of individual and family farms (Figure 3a and Figure 4).

In the post-Soviet era, the primary local centres correspond to the main urban hubs of the coastal region, specifically Klaipėda, Palanga, Kretinga, Gargždai, and Šilutė (Figure 3c).

This transformation can be attributed to the agricultural and land planning policies implemented by the Soviet Union, which resulted in the dissolution of individual and family farms and their replacement with collective farms, such as ‘sovkhoz’ and ‘kolkhozes’ in the early 1950s (Gadal, 2011; Köll, 2024). The structure of the pre-Soviet individual and family farms is comparable to these main urban centres in terms of accessibility, underscoring their historical significance in the economic and territorial development of the Klaipėda region. Accessibility analysis supports this conclusion: the top 25% of values for the pre-Soviet network exceed 1.30, while the post-Soviet network shows values of 1.70, indicating a more organic and interconnected network prior to Soviet policies of land collectivisation.

The reconfiguration of the network illustrates the process of metropolisation taking place in the Baltic countries (Ubarevičienė, 2018). This process

commenced during the Soviet period when territorial planning adhered to a ‘hierarchical centre-periphery’ model (Vanagas et al., 2002). Klaipėda was designated as a regional centre, intended to be linked

<figure><img src="image_45.png" /><figcaption>Figure 4: Network associated with local centres: the case of individual/family farms during the pre-Soviet period.</figcaption></figure>
<figure><img src="image_46.png" /><figcaption>Figure 5: Spatial evolution of networks relating to territorial centre of gravity between the (a) pre-Soviet, (b) Soviet, and (c) post-Soviet periods.</figcaption></figure>

to secondary regional centres through a network of smaller cities like Palanga or Kretinga. In the post-Soviet period, this process accelerated as the Soviet territorial framework persisted, leading to heightened urbanisation of the main urban centres. The findings from the Betweenness index at the local level (section 4) are particularly illuminating, revealing a more pronounced hierarchy of networks emerging from the Soviet era onwards.

## 5.2 Territorial Centre of Gravity

The second spatial structure is associated with grid tiles that exhibit high averages for the foreground network index (Figure 5).

These identified grid tiles correspond to the territorial centre of gravity, highlighting the most efficient networks at the regional level in terms of accessibility — networks that bolster regional economic development. The shift of the territorial centre of gravity from the south to the centre of the region is the main change observed between the pre-Soviet and post-Soviet periods (Figure 5).

Two complementary interpretations can be made regarding this spatial evolution. The first pertains to

geopolitical changes within the region: during the pre-Soviet period, the southern part of the coastal region was under Prussian control and was an integral part of East Prussia, with Königsberg (now Kaliningrad) as its capital (Figure 9a). The establishment of the Lithuanian state after World War I led to a severance of the historical connections with present-day Kaliningrad oblast. The designation of Klaipėda as the administrative centre of the coastal region (currently known as ‘Klaipėda apskritis’) marks a significant shift in the network’s centre of gravity toward the heart of this coastal region.

A second interpretation pertains to the economic transformations that have occurred since the 20th century. During the pre-Soviet period, the region's economy relied on agriculture, with the most developed networks corresponding to areas of intense agricultural activity. The presence of individual and family farms as local centres (section 5.1) supports this notion. However, in the post-Soviet era, the economy underwent a substantial transformation, realigning itself around industrial and service sectors. Consequently, the network's centre of gravity is now found in the primary industrial and tourist hotspots,
<figure><img src="image_47.png" /><figcaption>Figure 6: Spatial evolution of networks relating to secondary centres in transition between the (a) pre-Soviet, (b) Soviet, and (c) post-Soviet periods.</figcaption></figure>

specifically the port of Klaipėda and the seaside resorts of Palanga and Šventoji.

It is also noteworthy that these economic shifts are intricately linked to the development of specific transport infrastructures. In this respect, motorways have played a crucial role in defining the network's centre of gravity in the coastal region (Figure 5c). The first key route (east-west) is the 'A1' motorway, which connects Klaipėda (on the Baltic Sea) with Odesa (on the Black Sea) and Moscow. According to the Soviet military maps from 1985, the motorway's construction was incomplete, aside from the sections near Klaipėda. The second major route (south-north) is the 'A13' motorway, which links Klaipėda and the northern part of the coastal region with Latvia. The third route (east-west) establishes a connection between Palanga and the regional centre of Šiauliai (outside the study area) via the 'A11' motorway, passing through Kretinga. The development of these routes has further influenced the local hierarchy of the network, as evidenced by the Betweenness index during both the Soviet and post-Soviet periods (section 4).

The trends for these first two spatial structures mirror those identified by Strano et al. (2012) in the

suburbs of Milan, showcasing a transformation of the territory from rural areas to urban-industrial zones, and eventually to metropolitan regions.

## 5.3 Secondary Centres in Transition

The third spatial structure is associated with grid tiles that exhibit, on average, low to medium background network indexes when compared to other spatial structures (Figure 6). Across all periods analysed, the average standardised indexes for the spatial structure of the main local centres consistently exceed those of this structure: 4.15 compared to 0.66 for NQPD, 2.72 compared to 1.02 for the Length, and 4.57 compared to 0.32 for Betweenness.

This spatial structure denotes networks that are in a 'transition phase' towards main local centres (section 5.1) or peripheral areas (section 5.4). For instance, during the post-Soviet period, this cluster was situated in peri-urban zones surrounding key urban centres like Klaipėda or Kretinga (Figure 6c), which can be classified as secondary centres.
<figure><img src="image_48.png" /><figcaption>Figure 7: Spatial evolution of networks related to peripheral territories between the (a) pre-Soviet, (b) Soviet, and (c) post-Soviet periods.</figcaption></figure>

## 5.4 Peripheral Territories

The fourth spatial structure is characterised by grid tiles exhibiting low average indexes, irrespective of the type of network (Figure 7).

The grid tiles identified within this spatial structure are linked to the peripheral networks at the regional scale. For instance, during the post-Soviet period (Figure 7c), these peripheral networks were found in the agricultural and semi-natural areas of the Niemen delta, as well as the riparian zones along the Niemen River, delineating the southern border with Russia. In comparison to the pre-Soviet era, contemporary agricultural practices are more modern but less intensive, reflecting a trend of agricultural decline (Lekavičiūtė and Gadal, 2009; Spiriajevas, 2014).

Moreover, the motorway infrastructure, which facilitates more efficient access to the network, is confined to the northern part of the coastal region. This overall marginalisation of the southern region may partially account for the observed decline in the foreground network index.

During the pre-Soviet period (Figures 7a and 9a), it is noteworthy that the peripheral networks corresponded to territories under Russian control.

<figure><img src="image_49.png" /><figcaption>Figure 8: Patterns of spatial organisation between Prussia and Russia in pre-Soviet Lithuania.</figcaption></figure>

Two distinct patterns of spatial organisation appear to emerge between the two countries (Figure 8): one explanation for this could relate to the
<figure><img src="image_50.png" /><figcaption>Figure 9: Spatiotemporal modelling of spatial structures relating to road network in the Klaipėda region between (a) the pre-Soviet period, (b) the Soviet period, and (c) the post-Soviet period.</figcaption></figure>

differing timelines of agrarian reform. In Prussia, the reform aimed at developing the familial farming model was implemented earlier (in the early 19th century) compared to Russia, where the reform was introduced later (in the late 19th century), and the traditional street village model was retained (Jespen et al., 2015).

# 6 CONCLUSIONS AND PERSPECTIVES

This research analyses the evolution of spatial structures and territorial organisation in the Klaipėda region since the 20th century, utilising the Generic City concept to model road networks.

By distinguishing between foreground and background networks with indexes tailored for regional analysis, this study measures the relationship between the spatial configuration of space and its associated functions, thereby illustrating various processes at play within the Klaipėda territory.

The significant territorial restructuring observed during the pre-Soviet and post-Soviet periods is particularly evident in the integration of local urban

centres along the coastal region, driven by the dynamics of metropolisation. This metropolisation was accompanied by a shift in the region's economic function from agriculture to industry and services. The development of motorway infrastructure further bolstered this territorial growth, enhancing regional accessibility while marginalising agricultural areas that once formed the core of the territory. Additionally, the collectivisation reforms and geopolitical shifts following the disintegration of the former province of East Prussia contributed to this marginalisation.

In this study, the Generic City indexes — modified and adapted for territorial analysis — proved effective in highlighting the interconnected political, economic, social, and cultural factors that influence the spatial and territorial organisation of the Klaipėda region.

The reliability of the modelling was partly validated by the alignment of results with existing scientific literature on this region. Incorporating supplementary data, such as built-up areas, land use, and land cover, could further strengthen the model if similar trends are identified. However, the indexes associated with the Generic City concept, modified
for regional analysis, can only be generalised through
examination in other territorial contexts exhibiting
comparable dynamics. In this respect, the Baltic Sea
region presents intriguing opportunities, particularly
considering the rapid, intense and complex impacts of
political, economic, social, and cultural changes on
spatial and territorial organisation.

In this regard, the historical application of
generative models remains constrained when they are
trained on overly general databases. The approach
developed in this research, which utilises
mathematical graph models, has been tailored from
generic indexes to conduct a thorough analysis of
existing structures at a territorial scale. This method
considers the complexities of the Baltic region,
characterised by the interplay of various cultural
influences, including Prussian, Russian, Soviet, and
post-Soviet elements.

From a methodological standpoint, this research
enables a connection between historical spatial
structures and specific territorial functions, such as
agricultural activities. Furthermore, spatiotemporal
modelling of road network structures could serve to
enhance missing or incomplete historical data in
retrospective modelling studies.

ACKNOWLEDGEMENTS

This Research is supported by the Science Council of
Lithuania's "Next Generation of Multi-Objective
Satellite Image Recognition Algorithms for Climate
Monitoring" program (LMT NEUTRINO) and the
CNES TOSCA THRISNA ISEULT.

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# Developing a Geospatial Framework for Calculating a 15-Minute City Index (FMCI): The Case of Quezon City

Carlo Angelo R. Mañago<sup>a</sup>, Marielle G. Nasalita<sup>b</sup>, Cesar V. Saveron<sup>c</sup>, Ynah Andrea D. Sunga<sup>d</sup>
and Alexis Richard C. Claridades<sup>d,e,*</sup>

Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines
{crmanago, mgnasalita, cvsaveron, ydsunga, acclaridades}@up.edu.ph

**Keywords:** 15-Minute City, Urban Life Quality, 15-Minute City Index, Service Area, Network Analysis, Pedestrian Network.

**Abstract:** The 15-minute city concept is a measure of the quality of urban life based on proximity, sustainability, and sociality. This study proposes a geospatial framework for calculating the 15-minute city index (FMCI) aimed to measure the accessibility of its residents to six social functions, which include living, working, supplying, caring, learning, and enjoying. Quezon City, Philippines, was chosen for its urban characteristics that aligned with this vision and served as the study area. To account for pedestrian needs, age-based weights were assigned to the social functions, and service areas were mapped using a uniform walking speed. FMCI values were calculated based on weighted social functions and barangay population distribution by age group. Results revealed that 39% of Quezon City's barangays achieved a perfect FMCI score of one, indicating access to all six functions within a 15-minute walk. Positive spatial autocorrelation indicated the clustering of barangays with similar FMCI values, with hot spots in the southern and cold spots in the northern parts of the city. These findings offer insights for policymakers in improving urban life quality. The adaptable FMCI framework can be applied to other urban areas to assess service accessibility, considering residents' needs.

## 1 INTRODUCTION

The 15-minute city is a population-oriented urban development concept that has gained popularity in recent years. Its primary idea is that the accessibility to essential services of the residents should be within a 15-minute radius of their homes, whether by walking, biking, or utilizing public transportation (Poorthuis & Zook, 2023). It is a multi-reformative urban model that addresses the decentralization of a city, reduction of carbon footprint, and social and economic integration (ArchDaily, 2024). The COVID-19 pandemic brought renewed focus to this pedestrian-centered idea, as lockdowns underscored the need for easily accessible amenities amid limitations on mobility and public transportation (Akrami et al., 2024).

Moreno et al. (2021) claim that the ability of residents to access six essential urban social functions (SF), which include (a) living, (b) working, (c) supplying, (d) caring, (e) learning, and (f) enjoying, in a 15-minute space-time frame is linked to a higher quality of life. This concept connects the proximity and integration of the six essential functions to the enhancement of well-being, sociality, and sustainability, which emphasizes the purpose and significance of the 15-minute city to offer a high-quality urban life to the residents.

For the practical and effective implementation of the 15-minute city, each societal function should be broken down into subcategories and linked to corresponding specific uses, activities, and facilities (Moreno et al., 2023). Table 1 provides a summary of the elements and facilities associated with six SFs.

<footnote>a https://orcid.org/0009-0001-7799-1645</footnote>

<footnote>b https://orcid.org/0009-0001-1777-7161</footnote>

<footnote>c https://orcid.org/0009-0006-0492-4608</footnote>

<footnote>d https://orcid.org/0009-0008-5763-5070</footnote>

<footnote>e https://orcid.org/0000-0001-9826-4271</footnote>

<footnote>*Corresponding author.</footnote>
Considerably, population distribution per age distribution of a city is an important demographic characteristic to be considered in assessing a 15-minute city (Moreno et al., 2023). For instance, a child must have access to learning or enjoying SFs, while the elderly must have access to the caring SF. Assessment of accessibility within the neighborhood requires incorporation of the origins of mobility needs, which the population distribution represents. This ensures that services are located close to the people who are the users of these services (Caselli, 2021). Thus, this research project will incorporate population data to facilitate the assessment.

<table><caption>Table 1: Elements and facilities are integrated into the six SFs.</caption><thead><tr><td>Social Function</td><td>Subcategories</td></tr></thead><tbody><tr><td>Living</td><td>Housing<br>Energy<br>Waste Management<br>Transportation<br>Services<br>Infrastructure</td></tr><tr><td>Working</td><td>Environment<br>Access<br>Diversity<br>Services</td></tr><tr><td>Getting Supplies</td><td>Food<br>Non-food-related consumption<br>Public services</td></tr><tr><td>Enjoying</td><td>Holidays<br>Culture<br>Leisure<br>Association</td></tr><tr><td>Learning</td><td>Access<br>Availability<br>Performance<br>Guide</td></tr><tr><td>Caring/Being Healthy</td><td>Access to care<br>Prevention<br>Emergency<br>Living environment<br>Wellness<br>Sport<br>Pollution</td></tr></tbody></table>

Over the past few years, the 15-minute city concept has gained significant support from various countries due to its urban planning benefits. Cities such as Barcelona, Oxford, and Paris have embraced this model (Vaughn, 2023). In China, the government has prioritized the development of walkable neighborhoods, ensuring that essential public services are accessible within a 15-minute walk. This initiative not only promotes walking behaviors and enhances overall health but also highlights the critical role of such neighborhoods in reducing the risk of obesity and non-communicable diseases (NCDs) among residents (Weng et al., 2019).

Scholarly research demonstrates varying approaches to measuring accessibility in urban contexts, with some studies favoring network distances (Birkenfeld et al., 2023; Fazio et al., 2023; Gorrini et al., 2023; Knap et al., 2023) while others employ Euclidean distances, largely due to the computational challenges posed by road network data (Gaglione, 2022; S. Zhang et al., 2022). Network distances are generally preferred as they provide more realistic and precise results by reflecting the actual paths residents travel to access urban amenities (Papadopoulos et al., 2023). In contrast, Euclidean distances are deemed inadequate for spatial analyses within road networks, as they fail to account for critical factors such as road length and travel time, leading to underestimation (Shahid et al., 2009).

A variety of indicators have been used to evaluate the principles underlying the 15-minute city concept. For example, Starrico (2022) assessed service accessibility by calculating the percentage of the population with access to at least one amenity of the same type within a 15-minute walking radius, highlighting the need to consider population coverage. The selected metrics aid in optimizing the balance between service count, spatial distribution, and area coverage relative to user locations. Starrico's findings revealed significant disparities, with certain areas over-supplied with amenities while others lacked sufficient services despite being populated.

Similarly, Gaglione et al. (2022) examined accessibility by analyzing population density in conjunction with urban amenities. By overlaying population density data onto service area polygons generated through urban amenity analysis, they identified the proportion of the population served or underserved within a 15-minute walking distance. These reinforce the utility of population distribution as a key metric for evaluating the "density" dimension of the 15-minute city, which is the number of residents who can access available resources, infrastructure, and spaces (Moreno et al., 2021).

In the Philippines, the Quezon City government is actively considering the adoption of the "15-minute city" concept as an important part of its efforts to foster a livable, environmentally conscious, and sustainable community for its residents. This endeavor of the local government unit aims to stimulate the economy locally through the decentralization of conventional urban services and their increased accessibility within local neighborhoods (Mateo, 2023). This study aims to develop a geospatial data-based method for evaluating the "15-minute city" concept in the case of Quezon City's urban setting.
This study aims to create a 15-Minute City Index (FMCI) that quantifies how closely a region or area approaches the ideal of 15-minute walking accessibility to six key SFs: living, working, caring, supplying, learning, and enjoying. The research focuses specifically on walking as the mode of transportation, using a walking speed tailored to Metro Manila, Philippines, with a case study in Quezon City. The index calculation, based on the barangay, the smallest administrative unit in the Philippines, incorporates barangay-level population distribution data by age group and the weighted needs specific to each group.

The FMCI is scored between 0 and 1, where a score of 0 represents no accessibility to any of the six SFs, and a score of 1 signifies full access to all six functions within a 15-minute walking distance, meeting the age-specific needs of the population. With this, the paper aims to perform service area network analysis or isochrone analysis for the six SFs, to develop a geospatial framework for the 15-minute city, define the FMCI and model its dependence on age-group-specific population distribution, calculate the FMCI for each barangay, the smallest administrative unit in the Philippines, and to examine spatial autocorrelation to identify clustering patterns of FMCI values.

# 2 METHODOLOGY

## 2.1 Geospatial Framework

Figure 1 presents the geospatial framework for developing the 15-minute city index in the selected case study area of Quezon City. This displays the interplay and relation of the five key components of the FMCI, which include age group classification, pedestrian network, population distribution, the six SFs, and walking speed, based on literature. This paper adopts the six SFs defined by Moreno et al. (2021) to identify urban amenities offering various services, which will be referred to as 'points of interest' in this study.

Network analysis, in particular service area analysis, is employed to assess the proximity of services with time as the cost (ESRI, n.d.). Consequently, this required the incorporation of road network data. To emphasize the pedestrian-centric principle of the 15-minute city, walking was chosen as the transportation mode. The selected walking speed was utilized to generate service areas around the points of interest.

<figure><img src="image_51.png" /><figcaption>Figure 1: Geospatial framework for calculating FMCI.</figcaption></figure>

Moreover, while many existing studies utilized population household locations as starting points, this research took on a different approach inspired by
Gaglione et al. (2022), wherein the origins of proximity analysis were points of interest. As such, the regions reached within 15 minutes from the points of interest delineate the population household locations within the service area. The calculation of the 15-minute city index depended on the age-based weight of SFs and population distribution of the age classification with respect to the six SFs, while the analysis of results involved spatial statistics. These will be discussed further in the subsequent sections. It is important to note that this framework was applied in the case of Quezon City, Philippines.

## 2.2 Data Gathering

### 2.2.1 Age Group Classification

The age group classification in this study is based on the common age structure used for population distribution in the Philippines (Philippines Age Structure - Demographics, 2020), which is 0 - 14 years, 15 - 34 years, 35 - 54 years, 55 - 64 years and 65 - years and above. Given the focus on walking as the mode of transport for assessing the 15-Minute City Index (FMCI) in Quezon City, the first age group has been adjusted to 7-14 years. This modification aligns with the onset of independent mobility among children, which typically begins around age seven (Schoeppe et al., 2015). This adjustment is crucial for the assessment, as it ensures the evaluation considers the accessibility of services for children capable of independent walking. By doing so, the study ensures that points of interest (POIs) are realistically reachable within a 15-minute walk for this demographic.

Moreover, considering the median marrying age in Quezon City, which is around 29 to 30 years (PSA, 2023), the classification has been further adjusted to better categorize and address the specific needs of those below and above these ages. Below is the revised and final age group classification which will be used in this study:

* 7 - 14 years

* 15 - 29 years

* 30 - 54 years

* 55 - 64 years

* 65 years and above

### 2.2.2 Road Data and Land Use

Road data for Quezon City was acquired using the QuickOSM plugin in QGIS, which enables the retrieval of freely available OpenStreetMap (OSM) data (QGIS Documentation v: 3.34, n.d.), which is completely available for the area. To properly

account for facilities outside the study area that may be within the proximity of areas that are near said boundaries, a one-kilometer buffer was applied, allowing the inclusion of routes extending beyond Quezon City's immediate boundaries. Figure 2 illustrates the raw road data prior to data cleaning and network establishment.

<figure><img src="image_52.png" /><figcaption>Figure 2: Quezon City's Road network is extended by a one-kilometer buffer.</figcaption></figure>

### 2.2.3 Population Distribution

The population data utilized in this study originates from the 2020 Barangay Census of Quezon City, sourced from the Humanitarian Data Exchange platform and managed by the Office for the Coordination of Humanitarian Affairs (OCHA) Philippines. It includes information on the total population as well as population breakdowns by age and sex for each barangay in Quezon City.

### 2.2.4 Points of Interest

In this paper, POIs from the open dataset are classified into the six SFs defined by Moreno et al. (2023). These functions represent key aspects of daily life and urban needs, with each category encompassing specific amenities that serve these roles. This paper adopts this methodology by focusing on the available facilities related to the set of terms and concepts specifying each SF, based on
Table 1. Table 2 presents the POIs categorized under each SF and their respective formats and sources.

<table><caption>Table 2: Information on the POIs.</caption><thead><tr><th>Social Function</th><th>Points of Interest</th><th>Data Format</th><th>Data Source</th></tr></thead><tbody><tr><td rowspan="3">Living</td><td>LRT1 & 2, MRT3 Train Stations</td><td>Point</td><td>Land Transportation Office</td></tr><tr><td>QC Bus Stations</td><td>Point</td><td>Quezon City Government</td></tr><tr><td>EDSA Carousel Bus Stations</td><td>Point</td><td>Edsa Busway</td></tr><tr><td rowspan="2">Working</td><td>Barangay Halls & Quezon City Hall</td><td>Point</td><td>OpenStreetMap</td></tr><tr><td>Commercial, Industrial, & Institutional areas</td><td>Polygon</td><td>Quezon City Government</td></tr><tr><td rowspan="2">Supplying</td><td>General shops</td><td></td><td></td></tr><tr><td>Supermarket</td><td></td><td></td></tr><tr><td rowspan="3">Caring</td><td>Convenience Stores</td><td></td><td></td></tr><tr><td>Public Market</td><td></td><td></td></tr><tr><td>Hospitals</td><td></td><td></td></tr><tr><td rowspan="2">Learning</td><td>Pharmacies</td><td></td><td></td></tr><tr><td>Health Centers</td><td></td><td></td></tr><tr><td rowspan="7">Enjoying</td><td>K-12 Public Schools</td><td></td><td></td></tr><tr><td>Museum</td><td>Point</td><td>OpenStreetMap</td></tr><tr><td>Picnic sites/ Park</td><td></td><td></td></tr><tr><td>Theme park, Water park</td><td></td><td></td></tr><tr><td>Zoo</td><td></td><td></td></tr><tr><td>Amusement</td><td></td><td></td></tr><tr><td>Arcade</td><td></td><td></td></tr><tr><td rowspan="5">2.2.5 Walking Speed</td><td>Garden</td><td></td><td></td></tr><tr><td>Playground</td><td></td><td></td></tr><tr><td>Swimming</td><td></td><td></td></tr><tr><td>Areas</td><td></td><td></td></tr><tr><td>Malls</td><td></td><td></td></tr></tbody></table>

In a walkability study by Gerilla (1995), a mean walking speed of 70.62 meters per minute, or approximately 4.23 kph, was determined for Metro Manila. This speed was established by estimating walking distance in the region and has since been used in other walkability studies in the Philippines and Asia to reflect the walking speed in Metro Manila (Mateo-Babiano & Ieda, 2007; Tolentino & Sigua, 2022). This speed was uniformly used to create service areas from sets of POIs.

## 2.3 Data Preparation

### 2.3.1 Getting Points for the Working Social Function

Figure 3 shows the land use (LU) classification of Quezon City obtained from the city government. Based on local regulations, one location can only be classified into one LU. Based on this, commercial, industrial, and institutional areas within Quezon City are designated as the working areas within the city to simplify the identification of the places of work. This assumes that people working from home or those with no fixed places of work (delivery workers, etc.) are not considered.

<figure><img src="image_53.png" /><figcaption>Figure 3: Land use classification of Quezon City.</figcaption></figure>

These areas are converted into points of interest for the working SF by generating points along the boundary of the polygon features. Apart from the boundary points serving as entry points to these areas, centroids were not used to avoid over-generalization of the POI. The interval of the generated points is based on the maximum distance reached in 15 minutes using our assumed walking speed based on Equation 1.

$$ \text{Max. Distance} = \text{walking speed} \times \text{time cost} \quad (1) $$
where:

$$ \text{walking speed} = 70.62 \text{ [m/min]} $$

$$ \text{time cost} = 15 \text{ [min]} $$

As a result, points along the working area boundary were generated at 1059.3-meter intervals to reflect the 15-minute temporal distance between each adjacent point.

## 2.3.2 Calculating the Age Group-Based Population per Barangay

The population for ages 0-6 was subtracted from the total population, consistent with the adopted age ranges. Given that the raw population data is aggregated at ranges finer than what is adapted in this study, the population for each age group was obtained by summing the population data within the specified range. The ratio of each age group relative to the new total population per barangay was also calculated.

## 2.3.3 Weighting of Social Functions

Pedestrian characteristics are important in studies involving walkability (Gorrini et al., 2023). Therefore, in this study, a set of weights for each SF was established for each group. To accomplish this, seven experts in various fields relevant to the study, including sociology, geography, geomatics, and urban planning, are consulted to characterize the population for each age group. The experts were requested to rank each SF based on its importance for each age group. Table 3 shows the resulting average rank for the SFs per age group.

<table><caption>Table 3: Average rank of SFs per age group.</caption><thead><tr><th rowspan="2">Social Function</th><th colspan="5">Ranking for each age group</th></tr><tr><th>7-14</th><th>15-29</th><th>30-54</th><th>55-64</th><th>≥ 65</th></tr></thead><tbody><tr><td>Living</td><td>3</td><td>2</td><td>1</td><td>1</td><td>2</td></tr><tr><td>Working</td><td>6</td><td>5</td><td>2</td><td>2</td><td>6</td></tr><tr><td>Supplying</td><td>5</td><td>6</td><td>3</td><td>5</td><td>4</td></tr><tr><td>Caring</td><td>4</td><td>4</td><td>3</td><td>3</td><td>1</td></tr><tr><td>Learning</td><td>1</td><td>1</td><td>6</td><td>6</td><td>5</td></tr><tr><td>Enjoying</td><td>2</td><td>3</td><td>3</td><td>4</td><td>3</td></tr></tbody></table>

Then, the weight of each rank was calculated through Equation 2. Table 4 shows the computation of the weights for each rank based on using values from Table 3 in this equation.

$$ W_i = \frac{1}{m} \sum_{k=n}^{m} \frac{1}{k} \qquad (2) $$

where:

$$ W_i = \text{weight of SF } i $$

$$ m = \text{total number of SFs} $$

$$ n = \text{rank of SF } i $$

<table><caption>Table 4: Calculation of weight per rank.</caption><thead><tr><th>Rank</th><th>Calculation</th><th>Weight</th></tr></thead><tbody><tr><td>1</td><td>$\frac{1}{6} \times (1 + \frac{1}{2} + \frac{1}{3} + \frac{1}{4} + \frac{1}{5} + \frac{1}{6})$</td><td>0.4083</td></tr><tr><td>2</td><td>$\frac{1}{6} \times (\frac{1}{2} + \frac{1}{3} + \frac{1}{4} + \frac{1}{5} + \frac{1}{6})$</td><td>0.2417</td></tr><tr><td>3</td><td>$\frac{1}{6} \times (\frac{1}{3} + \frac{1}{4} + \frac{1}{5} + \frac{1}{6})$</td><td>0.1583</td></tr><tr><td>4</td><td>$\frac{1}{6} \times (\frac{1}{4} + \frac{1}{5} + \frac{1}{6})$</td><td>0.1028</td></tr><tr><td>5</td><td>$\frac{1}{6} \times (\frac{1}{5} + \frac{1}{6})$</td><td>0.0611</td></tr><tr><td>6</td><td>$\frac{1}{6} \times (\frac{1}{6})$</td><td>0.0278</td></tr></tbody></table>

Based on these results, the final weights of each SF for each age group are established. It is important to note that from Table 3, experts ranked supplying, caring, and enjoying SFs equally for the age group 30-54 years old, thus having the same weight. Since the 3 SFs are tied in ranking, the weights for ranks 3-5 were added and divided by 3 and assigned to the three SFs to make sure the sum of weights is still 1. The results are presented in Table 5.

<table><caption>Table 5: Weights of SFs per age group.</caption><thead><tr><th rowspan="2">Social Function</th><th colspan="5">Weights for age group</th></tr><tr><th>7-14</th><th>15-29</th><th>30-54</th><th>55-64</th><th>≥ 65</th></tr></thead><tbody><tr><td>Living</td><td>0.1583</td><td>0.2417</td><td>0.4083</td><td>0.4083</td><td>0.2417</td></tr><tr><td>Working</td><td>0.0278</td><td>0.0611</td><td>0.2417</td><td>0.2417</td><td>0.0278</td></tr><tr><td>Supplying</td><td>0.0611</td><td>0.0278</td><td>0.1074</td><td>0.0611</td><td>0.1028</td></tr><tr><td>Caring</td><td>0.1028</td><td>0.1028</td><td>0.1074</td><td>0.1583</td><td>0.4083</td></tr><tr><td>Learning</td><td>0.4083</td><td>0.4083</td><td>0.0278</td><td>0.0278</td><td>0.0611</td></tr><tr><td>Enjoying</td><td>0.2417</td><td>0.1583</td><td>0.1074</td><td>0.1028</td><td>0.1583</td></tr><tr><td>Sum</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td></tr></tbody></table>

## 2.3.4 Preparation of Network Dataset

To generate the pedestrian road data from the raw road dataset from OSM, features that are accessible to pedestrians are retained. Hence, the network data included pedestrian lanes, footbridges, and paths. Primary and trunk roads were also still utilized, but only in the areas where there were walkable paths, such as exterior lanes. Google Maps and Google Streetview were used to verify walkable paths across the road network. Roads accessible to vehicles only are removed, with motorways like expressways, skyways, and U-turn slots removed from the network. Road features that were not in the raw OSM network were added and properly referenced. Extensive intersections were checked for proper pedestrian routes. Residential roads were included in the road network, along with some service roads that access
residential areas. Service roads that go into
institutional areas like schools and hospitals were
removed from the network.

**2.4 Network Analysis**

Service areas (SA) from each of the six sets of POIs
were generated via the Network Analyst tool of
ArcGIS using the 15-minute walking time cost as the
impedance on the finalized pedestrian network
dataset. The direction was set away from the
facilities, which were set to be the POIs.

## 2.5 Calculating the 15-Minute City Index

**2.5.1 Generation of Regions and Age Group-**
**Based Scoring**

<figure><img src="image_54.png" /><figcaption>Figure 4: Resulting regions from combining service areas.</figcaption></figure>

Each service area generated from 2.4 is assigned a score of 1 for the SF it represents and a score of 0 for the other SFs. In the context of this study, we define a region as an area covered by a unique set of service areas. To generate the overlapping regions of the service area polygons, the six service areas are combined. Figure 4 visualizes the combination of the service areas, where the colors of the features in the larger map show the number of service areas that cover said location.

This map, however, does not illustrate the regions
themselves. For example, two areas valued “3” on the

map may not be the same region since each may
include different sets of 3 service areas. In contrast,
the colors in the inset map visualize the unique set of
service areas that cover the location. Since each
region combines a set of service areas, it has five
binary scores, each containing 1 (for an SA present in
the region) or 0 (for an SA not in the region). These
binary scores can be transformed into age-based
scores by multiplying them by the scores in Table 5
to obtain age group-based (AGB) scores per region.

**2.5.2 Transforming Region Scores to Scores**
**per Barangay**

An area-based overlay operation is used to re-
organize the AGB scores per region into the
barangays. We can compute the percentage of
barangay areas that each region covers by dividing
the region within the barangay's area by the
barangay's area. The region's five AGB scores are
multiplied by the percentage of barangay area that the
region covers. Then, the intermediate scores of each
barangay are derived by summing all the
corresponding AGB of the regions belonging to that
barangay. At the end of this step, the barangay has a
set of five intermediate scores corresponding to the
five age groups.

According to Section 2.3.2, each barangay has an
attribute for the population per age group. The
intermediate scores were multiplied by the
corresponding age group percentages. Finally, the 15-
minute city index is obtained from the sum of these
weighted scores.

**2.5.3 Clustering Analysis**

To further support the FMCI generated per barangay,
its global and local spatial autocorrelation was
assessed through Moran's I statistics. Similar
parameters were applied for both global and local
spatial autocorrelation. In the analysis, the
Conceptualization of Spatial Relationships was set to
be inverse distance, which imposes a larger influence
on closer features compared to farther features.
Concerning the distance method, Euclidean distance
was applied since, at this point, solely the FMCI are
considered among barangays without regard to roads
and borders, thus only requiring a simple distance
between barangays. Moreover, the standardization
parameter was set as 'row,' which is appropriate for
polygon data, and for aggregated data, which applies
to barangays' FMCIs.
# 3 RESULTS AND DISCUSSION

## 3.1 Service Areas

Six maps representing the coverage area of the six SFs discussed were made, with a 100-meter tolerance distance for the network analysis. The generated maps are in Figure 5. It can be observed that the location of services is heavily concentrated in the southern part of the city. Notably, La Mesa Dam, a watershed area, is in the northern part of Quezon City. The presence of this watershed reservation explains the lack of points of interest and, consequently, low coverage of service areas, as displayed by the large space in the northern area.

## 3.2 Scoring Regions and Barangays

As discussed in Section 2.5.1, regions were generated from overlaying service area datasets, and AGB scores were obtained by summing corresponding

weights. Intermediate scores were determined following the methodology detailed in Section 3.5.2. Higher intermediate scores suggest a closer proximity to achieving a high 15-minute city index. Similar to scored regions, the specific needs of different age groups are considered in this part through the weights of SF.

The complete results of the intermediate score for each barangay, categorized by age group, are shown in Figure 6. Darker areas imply a higher number of overlapping service area datasets, as AGB scores are from the summation of weights. However, it is important to note that this does not necessarily indicate a higher population served in darker areas, as the population distribution of each age group has not yet been taken into consideration. Higher intermediate scores suggest a closer proximity to achieving a high 15-minute city index. These scores illustrate the specific needs of different age groups, which are considered in this part through the weights of the SFs.

<figure><img src="image_55.png" /><figcaption>Figure 5: Generated service areas and points of interest for each of the six SFs. Each dot represents a POI.</figcaption></figure>
<figure><img src="image_56.png" /><figcaption>Figure 6: Age-Group-Based Scores per barangay.</figcaption></figure>

## 3.3 15-Minute City Index

After summing up the products of each calculated intermediate score by the corresponding barangay population percentage for each age group, the 15-minute city index (FMCI) of each barangay is generated. Figure 8 shows the distribution of barangays categorized by FMCI ranges. These ranges are established through the division of the entire FMCI spectrum into five equal intervals, from the minimum to the maximum value.

Out of 142 barangays, 56 achieved a perfect FMCI of one, representing 40% of the total barangays in Quezon City. This indicates that residents of all age groups of these barangays have access to the six SFs within a 15-minute walk. Conversely, the lowest FMCI of 0.066 was recorded for Pasong Putik. Situated in the northern part of Quezon City near the Sierra Madre Mountain Range and La Mesa Dam, the low FMCI of Pasong Putik implies that this area has low accessibility to the services defined by the six SFs. Table 6 shows the complete list of barangays that achieved a perfect FMCI and those that recorded the three lowest FMCIs in Quezon City.

<figure><img src="image_57.png" /><figcaption>Figure 7: FMCI for each barangay in Quezon City.</figcaption></figure>
<table><caption>Table 6: Barangays with the highest and three lowest FMCI.</caption><thead><tr><th>FMCI</th><th>Barangay Names</th></tr></thead><tbody><tr><td>1<br>(highest)</td><td>Alicia, Amihan, Bagumbuhay, Balong Bato, Bayanihan, Blue Ridge B, Bungad, Claro, Damayan, Del Monte, Dioquino Zobel, Duyan-duyan, E. Rodriguez, East Kamias, Escopa I, Escopa II, Escopa III, Escopa IV, Kamuning, Katipunan, Krus Na Ligas, Lourdes, Maharlika, Malaya, Mariblo, Marilag, Masagana, Masambong, Milagrosa, Obrero, Paraiso, Pinagkaisahan, Quirino 2-A, Quirino 2-B, Quirino 2-C, Quirino 3-A, Ramon Magsaysay, Roxas, Sacred Heart, Saint Peter, San Antonio, San Martin De Porres, San Roque, San Vicente, Santa Teresita, Santo Domingo (Matalahib), Sienna, Sikatuna Village, Silangan, Tagumpay, Talayan, Tatalon, Teachers Village East, Veterans Village, Villa Maria Clara, West Kamias</td></tr><tr><td>0.333</td><td>Payatas</td></tr><tr><td>0.243</td><td>Bagong Silangan</td></tr><tr><td>0.066<br>(lowest)</td><td>Pasong Putik Proper</td></tr></tbody></table>

## 3.4 Global and Local Cluster Analysis

With the FMCI already defined for each administrative division, this section explores significant geospatial patterns to identify relationships concerning the index. The spatial variation of the FMCI across barangays was examined using spatial autocorrelation techniques. As shown in Figure 8, the Global Moran's I statistic of 0.485244 indicates positive spatial autocorrelation, suggesting a clustered distribution of FMCI values among barangays. The statistical significance of this result, supported by a z-score of 18.46 and a p-value near zero, confirms a less than 1% likelihood that the observed clustering occurred randomly. These patterns are also visually evident in the FMCI distribution map in Figure 7.

The positive spatial autocorrelation indicated by the Global Moran's I suggests that FMCI values are not randomly distributed across barangays but rather tend to cluster. This implies that areas with similar levels of access to SFs are geographically proximate, reflecting spatial dependencies due to factors like urban infrastructure, socioeconomic conditions, and geographic location. The significance of this result reinforces the reliability of the clustering patterns and provides a robust foundation for understanding spatial disparities.

Local Moran's I provided more granular insights, identifying barangays as hot spots (High-High or

HH), cold spots (Low-Low or LL), or outliers (High-Low or HL and Low-High or LH). Figure 9 illustrates these patterns. Among the 142 barangays, 77 in the southern part of Quezon City form HH clusters with high FMCI values, reflecting greater access to the six SFs. Conversely, 13 northern barangays form LL clusters. Outliers include 13 LH barangays with open spaces and 2 HL barangays near the watershed. Notably, the remaining 37 of 142 barangays are not statistically significant. Thus, results from the extensive FMCI hot spots suggest that the 15-minute city concept is consistently realized throughout most of the study area.

<figure><img src="image_58.png" /><figcaption>Figure 8: Global Moran's I spatial autocorrelation report of FMCI in Quezon City.</figcaption></figure>

The Local Moran's I analysis provides a deeper understanding of the spatial dynamics within Quezon City. The 77 HH barangays suggest that these areas benefit from better urban infrastructure, higher population density, and improved access to essential SFs. On the other hand, the 13 LL barangays indicate that natural features such as open spaces and the La Mesa Watershed limit access to these functions, creating spatial disparities in service provision. The outliers suggest that factors like land use (e.g., parks and cemeteries) and proximity to natural features play an important role in shaping access to social services in these areas and highlight the complexity of spatial patterns and the need for context-specific interventions to address inequities. The observed clustering supports the Global Moran's I results, emphasizing positive spatial autocorrelation among barangays. These findings highlight disparities in accessibility, with southern barangays benefiting from proximity to Metro Manila's urban core, while
northern barangays face limitations due to geographic
and land use constraints.

<figure><img src="image_59.png" /><figcaption>Figure 9: Cluster/Outlier Map based on Local Moran's I spatial autocorrelation of FMCI in Quezon City.</figcaption></figure>

4 CONCLUSIONS

This study explores the 15-minute city concept,
emphasizing the importance of providing residents
with accessible essential services within a 15-minute
radius, thereby enhancing urban quality of life. In
Quezon City, service areas for these functions were
generated using points of interest (POIs), producing
maps that revealed a concentration of accessibility in
the city's southern regions. To contextualize these
findings, a 15-minute city index (FMCI) was
developed, integrating geospatial and demographic
data, including population and age distribution. The
FMCI evaluates how well areas achieve ideal
accessibility to the six SFs.

Analysis showed that 57 of 142 barangays,
predominantly in the south, achieved an FMCI of 1,
signifying access to all six functions. In contrast,
northern barangays like Pasong Putik scored the
lowest (0.066) due to geographical and land use
constraints. Spatial autocorrelation using Moran's I
confirmed clustering patterns of FMCI values,
identifying significant hot spots in the south and cold
spots in the north.

The FMCI, grounded in a geospatial framework,
provides valuable insights for policymakers to
enhance urban life by addressing proximity,
sustainability, and social equity while aligning with
the 15-minute city concept (Moreno, 2019). To
improve future implementations, service area
generation can be refined by incorporating
walkability in paths and adjusting walking speeds per
age group. Additionally, the 1-km buffer can be
implemented as network buffer, not a Euclidean
buffer. Sensitivity analysis should also be performed
for the expert-given weights to reinforce its
reliability. To overcome boundary effects,
tessellation-based calculation can be performed
instead of calculating the FMCI per barangay, as
calculating FMCI at a finer spatial resolution, such as
the household level, may reveal more nuanced spatial
patterns. Lastly, incorporating service prioritization
within each SF can improve the ranking, tailoring
scores more effectively for different age groups.

ACKNOWLEDGMENTS

The authors would like to thank Gerardo Lanuza,
Rhea Lyn Dealca, Ma. Afrecita Nieva, Aurora Llige,
John Abletis, Jonathan Guevarra, Carlyn Ann Ibañez,
Dominique Sasha Amorsolo, and Kristian Karlo
Saguin for sharing their expertise for evaluating the
SFs for each age group. We would also like to thank
the Department of Science and Technology – Science
Education Institute (DOST-SEI) and the Engineering
Research and Development for Technology (ERDT),
and the Office of the Vice Chancellor for Research
and Development (OVCRD) of the University of the
Philippines Diliman for the faculty research
dissemination grant that supported the publication
and presentation of this paper.

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# A Luminance-Based Lane Marking Candidate Quality Assessment for Autonomous Driving in GIS Contexts

Pathum Rathnayaka<sup>a</sup>, Young Hun Kim<sup>b</sup>, In Gu Choi, Gi Chang Kim<sup>c</sup> and Duk Jung Kim<sup>d</sup>

U1 Geographic Information System, 282, Hagui-ro, Dongan-gu, Anyang-si, Gyeonggi-do, Republic of Korea
{pathum, yhkim, igchoi, gckim, djkim}@u1gis.com

**Keywords:** Computer Vision, Geo Information Systems, Image Processing, Lane Detection, Luminance, Lane Marking Quality, Contrast Ratio, Binary Ratio.

**Abstract:** The detection of lane marking candidates is crucial for autonomous vehicles and advanced driver-assistance systems (ADAS) as they deliver essential information for accurate lane following, localization, and route planning. Detecting these candidates becomes difficult when road conditions deteriorate or the marking paint is faded, sometimes making identification nearly impossible. While deep/machine learning (DL/ML) methods perform well in reliable detection, they often come with the need for extensive labeled datasets, substantial computational power, and intricate parameter adjustments. In this research, we present a method purely based on digital image processing to identify and evaluate lane marking candidates, thus avoiding the use of specialized reflectivity equipment (such as luminance meters) and bypassing complex DL/ML methodologies. Our pipeline initially identifies lane marking regions using a collection of image-processing techniques. It then subsequently evaluates their quality using two conditional metrics: a luminance-based contrast ratio and a white-to-black pixel ratio. Each candidate is categorized as good, bad, or ambiguous by comparing these metrics to empirically determined thresholds. Evaluations conducted on large sets of road images from conventional and urban highways in South Korea confirm the effectiveness of our proposed method. The system significantly reduces the dependence on time-consuming labor-intensive annotation, high-end hardware, and DL/ML expertise. We thus claim that our lightweight, deployable method effectively addresses a significant gap in luminance-centric lane candidate quality evaluation and can serve either as an independent solution or as a supplementary option to more sophisticated DL/ML systems in GIS and ADAS applications.

## 1 INTRODUCTION

Autonomous driving and ADAS systems represent the forefront of modern transportation technology. By integrating diverse sensors, powerful computing hardware, and sophisticated algorithms, these systems promise to enhance driver safety, alleviate traffic congestion, and ultimately enable fully self-governing vehicles. Within this ecosystem, lane markings are fundamental; they serve as reliable visual cues for both human drivers and onboard perception systems, facilitating tasks such as lane-keeping, lane-changing, and path planning. Consequently, improvements in detecting and evaluating lane markings directly enhance the accuracy and robustness of vehicular

decision-making.

Despite their importance, lane markings are often subject to adverse real-world conditions. In- clement weather (e.g., rain or snow) and prolonged wear due to traffic can degrade markings to the point where detection becomes nearly impossible. Al- though deep learning models (DL), particularly con- volutional neural networks (CNNs), have shown re- markable performance in lane detection tasks (Aly, 2008), these approaches require substantial annotated data, significant computational resources, and fine- tuning of parameters. Such demands can challenge resource-limited environments, such as small munici- pal projects or research laboratories with constrained budgets.

In contrast, several luminance-based lane detec- tion strategies have emerged that directly measure visual characteristics such as, brightness, reflectiv- ity, and contrast. While some of these approaches

<footnote>a https://orcid.org/0000-0003-3422-9770</footnote>

<footnote>b https://orcid.org/0000-0002-0665-2822</footnote>

<footnote>c https://orcid.org/0000-0002-8751-3743</footnote>

<footnote>d https://orcid.org/0000-0001-7016-4594</footnote>
use specialized instruments (e.g., luminance meters) or incorporate elements of machine learning (ML), fully luminance-driven pipelines remain relatively uncommon despite their potential compatibility with standard RGB cameras. By leveraging fundamental image processing principles and mimicking human brightness perception, luminance-based methods eliminate the need for expensive hardware and large-scale data annotation.

Against this background, this paper introduces a practical and deployable luminance-based solution. We propose a high-quality digital image processing pipeline that first isolates lane marking candidates and then classifies them using two straightforward yet informative metrics: (1) a contrast ratio (CR) that compares the mean luminance of the marking with that of its surroundings, and (2) a binary ratio (BR) that measures the proportion of white pixels within a thresholded image. This dual-metric approach yields an interpretable quality label—*Good*, *Bad*, or *Ambiguous*—that can be readily integrated into ADAS modules and GIS platforms.

In regions such as South Korea, official road assessments often rely on reflectivity-based devices or hybrid DL/ML approaches to evaluate marking conditions (Lee et al., 2010; Park, 2019). Some other technical materials mostly consider identifying pavement markings and street markings as in (Lee et al., 2024). These methods can be prohibitively expensive and logistically challenging, especially when dealing with diverse road geometries, variable weather conditions, or frequent repainting cycles. Our pipeline addresses these issues by operating with minimal hardware such as a single monocular camera and employing straightforward computer vision techniques. We anticipate that this approach will facilitate large-scale deployment and reduce overall system overhead, ultimately contributing to improved traffic safety and driver comfort.

The remainder of this paper is organized as follows. Section 2 reviews relevant literature on lane detection and marking quality assessment, emphasizing gaps in luminance-based approaches and their importance as an adaptable measuring approach. Section 3 details our proposed algorithm. The section is divided into subsections; stating our system architecture and motivation, defining the image processing steps for luminance computation and edge processing for lane marking candidate region extraction, and the luminance-based and binary-based pixel analysis for quality classification criteria. Section 4 presents experimental results obtained under varied illumination conditions, while Section 5 discusses the challenges encountered and potential avenues for future research.

Finally, Section 6 summarizes our key findings and their broader implications for GIS and ADAS applications.

# 2 RELATED WORKS

## 2.1 Global Overview of Lane Detection Approaches

Lane detection has long been a central topic in computer vision and robotics. Early approaches frequently employed geometric constraints, such as Inverse Perspective Mapping (IPM), to project a 2D image onto a top-down view for easier extraction of lane lines (Bertozzi and Broggi, 1998; Yoo et al., 2017; Youjin et al., 2018). Classical techniques relied on methods like the Canny edge detector (Canny, 1986), the Sobel operator (Kanopoulos et al., 1988), and other gradient-based techniques to highlight lane boundaries, with the Hough transform (Kiryati et al., 1991) subsequently grouping these edges into coherent lines or curves. For example, Aly (Aly, 2008) combined intensity thresholding with perspective transformations to achieve real-time lane marker detection under controlled urban conditions.

In recent years, DL/ML paradigms, primarily CNNs, have become prevalent. Models, such as LaneNet (Garnett et al., 2019), perform lane detection as a pixel-wise segmentation problem (Neven et al., 2018), and architectures like the spatial convolutional neural network (SCNN) (Pan et al., 2018) employ specialized layers to capture row-wise dependencies in lane structures. More advanced techniques leverage Transformers (Yang et al., 2024) or generative adversarial networks (GANs) to manage occlusions, curves, and lane merges. Although these data-driven approaches often yield superior performance on benchmarks such as TuSimple (TuSimple Inc., 2017), CULane (Pan et al., 2018), and LLAMAS (Garnett et al., 2019), they introduce high requirements for domain-specific training data, GPU processing power, and parameter optimization.

Hybrid methods have also been explored, integrating geometric heuristics with CNN-based feature extraction to reduce computational overhead while enhancing resilience to noise. However, these techniques generally presuppose stable road layouts or favorable lighting conditions. In adverse scenarios, such as nighttime or inclement weather, these hybrid methods may struggle to distinguish lane markings from environmental artifacts.
## 2.2 Practicality of a Luminance-Based Approach

Luminance-based lane detection offers a simple yet effective alternative by relying on pixel intensity variations. This approach is computationally efficient and circumvents the extensive training datasets and high computational demands associated with DL/ML models. Furthermore, because luminance-based methods utilize standard cameras and fundamental image processing techniques, they provide increased interpretability and easier debugging compared to black-box AI methods.

Recent studies, such as those reported in (Lee and Cho, 2023), highlight the importance of retro-reflectivity and luminance in improving lane detection. However, these approaches typically require additional hardware sensors, increasing system complexity and cost. Similarly, LiDAR-based methods (Certad et al., 2022) remove dependencies on ambient lighting but at the expense of high-cost sensors and prolonged data processing times. Also, the channel number of generated LiDAR sensor scans could lead to erroneous qualitative analyses, whereas complicated LiDAR fusion should be implemented to check a particular region as a whole.

On the other hand, luminance-based methods (particularly for mobile platforms like single-board computers with a single camera and simple sensor integrations) enable real-time lane detection with minimal computational demands. Their potential for seamless integration and cost benefits makes these methods highly attractive for use in resource-constrained environments.

## 2.3 Luminance-Based Quality Estimation

A growing body of literature (Smadi et al., 2008; Burghardt et al., 2021; Zhu et al., 2021; Zhu et al., 2024) emphasizes the role of luminance, or brightness-related metrics, in evaluating road infrastructure quality. Early works as of (Balali et al., 2015) and (Roman et al., 2015) demonstrated the correlation between luminance levels and driver response, while (Zhu et al., 2021) extended these principles to assess nighttime pavement markings, showing that reduced reflectivity directly impacts driver reaction times.

In the context of lane markings, Sultana et al. (Sultana et al., 2022) have addressed all kind of real-life environmental challenges to detect road lane markings and categorized them into four types. Munir et al. (Munir et al., 2021) proposed an encoder-

decoder architecture for lane marking detection using event camera images, which they tend to extract higher-dimensional features from images, robust to illumination variations. Huang et al. (Huang and Liu, 2021) further illustrated illumination invariant Hough-based technique, evaluating the robustness of brightness comparisons as cues for road network extraction.

However, one major research gap that persists in most of these researches is the application of luminance-based algorithms is only limited to lane marking candidate detection. The approach is not only for detection but also for the quantitative assessment of marking quality. Road maintenance agencies require objective metrics to flag deteriorated markings for repainting or to assess safety risks. While specialized reflectivity meters exist (for Testing and Materials., 2005; Commission Internationale de l'Eclairage, 2001), their high cost and labor-intensive operation limit their scalability. Our approach addresses this by using contrast ratio (CR) and binary ratio (BR) metrics—correlating closely with human perception of clarity—to provide an interpretable and efficient quality assessment for lane markings.

# 3 ALGORITHM PIPELINE

<figure><img src="image_60.png" /><figcaption>Figure 1: Overview of our proposed algorithm. The input image is processed in the first module—Processing Module—and it extracts all the lane marking candidates using advanced image processing techniques. All these candidates are then passed to and processed in the second module—Quality Assessing Module—to evaluate their quality.</figcaption></figure>

## 3.1 Motivation and System Architecture

The proposed pipeline, illustrated in Figure 1, comprises two primary modules. The first module employs advanced image processing techniques to detect potential lane marking candidates, while the second module assigns quality labels based on combined luminance contrast and binary ratio evaluations. This dual-layer approach not only identifies lane markings but also quantitatively measures their clarity in real-world conditions.

Our rationale for adopting an exclusively luminance-based approach is twofold:
<figure><img src="image_61.png" /><figcaption>Figure 2: Overview of the first module: lane marking candidates are detected using image processing techniques. The luminance image is created and new color values are applied as a LUT and normalized to make marking candidates more distinguishable. The 'V' channel is extracted from the normalized image, where binarization, edge detection, n, and contour extraction methods are applied to identify the proper candidate region. Hough transformation is next applied to identify the lane marking as the final output of the first module.</figcaption></figure>

* **Hardware Minimization:** The method avoids the need for specialized reflectivity meters, facilitating widespread adoption in standard camera-based ADAS or onboard vehicle systems. This makes this method a suitable approach, even if additional equipment, such as LiDARisre required to be integrated for further intensity-based processing.

* **Algorithmic Simplicity:** By relying on established computer vision operations, such as filtering, thresholding, and morphological analysis, we eliminate the requirement for large annotated datasets, extensive GPU training, and complex optimization procedures/parameter tuning required in many AI approaches.

The subsequent briefly explains our proposed method w.r.t two processing modules described in Figure 1.

## 3.2 Detecting Lane Marking Candidates

The process of lane marking candidate detection is depicted in Figure 2 (First Module in Figure 1). In this, we prefer generating a luminance image to detect lane marking candidates, instead of using raw RGB camera images directly. Luminance images are preferred over RGB images as they leverage inconsistencies that could hinder the detection of lane marking candidates where clear distinctions should be made between lane surfaces and road surfaces at varying lighting conditions.

The luminance L can be computed as:

$$L = sB \times 0.0722 + sG \times 0.7152 + sR \times 0.2126, \quad (1)$$

This enables us to have a clear idea of the perceptual relativity of the human non-linear response curve. Thus, a more suitable luminance calculation suffice for the requirements of this research can then be derived by
1. normalizing sRGB values into decimal within the range of 0.0 ~ 1.0,

$$ \mathrm{Norm}_{(R,G,B)} = \frac{\mathrm{sChannel}}{255} \qquad (2) $$

eliminating the limitations of 8-bit quantization.

2. computing gamma-encoded RGB values from normalized sRGB values,

$$ = \begin{cases} \frac{\mathrm{Norm}_{(R,G,B)}}{12.92} & \text{if } \mathrm{Norm} \le 0.03928 \\ \left( \frac{\mathrm{Norm}_{(R,G,B)} + 0.055}{1.055} \right)^{2.4} & \text{otherwise} \end{cases} \qquad (3) $$

following established sRGB protocols (Poynton, 2012).

3. And using gamma-encoded values to compute luminance values which align with human visual sensitivity as of:

$$ L = 0.2126 R' + 0.7152 G' + 0.0722 B'. \qquad (4) $$

Once the luminance for every pixel is computed, the image is recolored as a look-up table (LUT) to identify the regions of our interest. Once recolored, it was witnessed that the image contained in unnecessary noise; which could degrade our image processing pipeline. A bilateral filter (Tomasi and Manduchi, 1998) is applied to suppress these high-frequency noise data while preserving important boundaries of lane lines. The image is converted to the YUV color space, and the V channel is extracted to isolate brightness or chrominance data useful in identifying reflective or bright candidate's paint information.

A min-max normalization is applied to standardize V channel intensities to [0, 255], followed by a gamma correction (Poynton, 2012) to align the brightness perception more closely with human vision. The Otsu's thresholding (Otsu et al., 1975; Otsu, 1979) is used to segregate brighter lane markings from the darker road surface. Small noise artifacts are further eliminated using morphological opening and speckle filtering.

The Canny edge detector (Canny, 1986) is used to identify the edge boundaries of lane candidates. A morphological dilation is applied to connect all the disjoint dashed candidates. By this, we managed to minimize the effect of mildly faded lane markings. Edge contours are approximated using the Ramer-Douglas-Peucker (RDP) algorithm (Douglas and Peucker, 1973; Ramer, 1972) to reduce polygon complexity, and convex hulls are computed to ensure continuous bounding while eliminating irregular clusters. Contours failing to meet minimum area or width criteria are discarded.

Next, a probabilistic Hough transform (Kiryati et al., 1991) is applied to refine lines that approximate typical lane geometry (straight and gently curved),

adhering to parallel or near-parallel constraints. The surviving polygons, which are lane marking candidates, are superimposed on the original RGB image for real-time validation. These lane marking candidates are subsequently assessed for quality, as discussed next.

## 3.3 Lane Marking Quality Assessment

After extracting lane marking candidates, their quality is quantified using two conditional metrics:

1. mean luminance comparison (contrast ratio: CR)

2. white-to-black pixel comparison (binary ratio: BR).

By integrating these measures, we obtain a holistic assessment of how distinct or faded each marking is relative to its background.

The first criterion is inspired by human visual perception of brightness differences and obviates specialized reflectivity measurements, whereas the latter is a justification of white pixel count w.r.t black pixel count inside the detected lane marking. To assess the visual distinctiveness of a candidate lane marking region, we compute the contrast ratio between the average luminance inside the region and the average luminance of the surrounding background. The process proceeds as follows:

1. Region Isolation

* A binary mask is created from the polygon that bounds the candidate lane region. Pixels within this polygon are set to white (255), and all other pixels remain black (0).

* We apply this mask to the luminance image and crops the resulting masked region using the polygon's bounding rectangle, ensuring that only relevant pixels (i.e., those within the polygon) are retained.

2. Lane vs. Background Accumulation

* Within the bounding rectangle, each pixel's value is added to a lane or background sum depending on whether it lies inside (> 0) or outside (= 0) the polygon. The total luminance for each category is tracked, along with the number of pixels in each category.

3. Mean Luminance Computation

* After processing all pixels, the total luminance of the lane region is divided by the lane-pixel count to obtain the average lane luminance ($\bar{L}_{lane}$).

$$ L_{lane} = \frac{\sum (\text{lane intensities})}{\text{lane pixel count}} + 0.05 \qquad (5) $$
<figure><img src="image_62.png" /><figcaption>Figure 3: Overview of the second module: evaluating the contrast ratio (CR) for each candidate. An ROI from the lane candidate found at the first module is extracted and a simulated pure black-and-white region (0s and 255s only) is created to distinguish pixel values above the lane marking from its surrounding road surface. Mean values are used to compute the contrast ratio and the outcome is compared with pre-determined threshold values. This is the first qualitative conditional metric of our proposed quality evaluation.</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>Figure 4: Overview of the second module: assessing the binary ratio (BR) for additional clarity analysis. The candidate region with the surrounding road surface area is extracted. A simulated ROI of the same size with only having 0s and 255s is created as a reference mask, and the total number of white pixels is calculated. The total white and black pixels from the real candidate polygon area is estimated by comparing them g with a pre-assigned threshold value. The final white-to-black ratio is computed using these two values.</figcaption></figure>

* Likewise, the total luminance of the background region is divided by the background-pixel count to obtain the *average background luminance* ($\bar{L}_{bg}$).

$$L_{bg} = \frac{\sum(\text{ground intensities})}{\text{ground pixel count}} + 0.05 \quad (6)$$

* A small offset (e.g., 0.05) is added to both values to avoid division by zero and to mitigate extremely low pixel intensities.

## 4. Contrast Ratio

* We define the contrast ratio, CR, as the ratio of the average lane luminance, $\bar{L}_{lane}$, to the average background luminance, $\bar{L}_{bg}$:

$$CR = \frac{\bar{L}_{lane}}{\bar{L}_{bg}} \quad (7)$$

* A larger contrast ratio implies that the lane region is significantly brighter than its surroundings, enhancing its detectability. Conversely, values below a chosen threshold (e.g., 2.0) suggest that the lane may be difficult to discern from the background.

## 5. Binary Ratio

* To further assess the region's distinctiveness, the cropped mask is thresholded via Otsu's method to produce a binarized mask.
* The number of white pixels (whiteCount) and black pixels (blackCount) are then computed.

* The binary ratio, BR is determined by:

$$BR = 1.0 - \frac{\text{blackCount}}{\text{whiteCount}} \qquad (8)$$

* This ratio captures how many pixels remain bright (white) relative to dark (black) pixels within the thresholded candidate region. Values closer to 1.0 indicate a more clearly distinguished (i.e., whiter) region after thresholding.

## 6. Quality Verdict Classification

* Equations 1 ~ 8 ensure that intensity values within the candidate lane region and the local background context are accounted for, yielding a robust measure of how clearly the lane markings stand out from their surrounding background. This combination provides a good conditional criterion to make a distinct verdict whether the candidate is of good condition, bad condition, or in a state in between.

* Experimenting with a multiple of input images, and with thorough condition evaluations by naked human eye, a cut-off value range suitable for making verdicts of lane markings is determined for both CR and BR.

* If CR < 2.0; the region is deemed to exhibit insufficient contrast and thus, assessed as of Bad CR.

* If 2.0 ≤ CR < 3.0; the region possesses borderline contrast and thus, assessed as of Ambiguous CR.

* If CR ≥ 3.0; the region is considered to have sufficiently high contrast and thus, assessed as of Good CR.

* After the CR evaluation, then it is checked against the BR condition. If BR < 0.17; the region is assessed as of Bad BR.

* If 0.17 ≤ BR < 0.49; the region is assessed as of Ambiguous BR.

* If BR ≥ 0.5; the region is assessed as of Good BR.

* A candidate is classified as Good Quality only if both CR and BR meet the “good” thresholds, Bad Quality if both fail, and Ambiguous Quality in all other cases. These thresholds were established after extensive pilot testing and visual inspections across a diverse set of road images.

# 4 EXPERIMENTS AND RESULTS

To rigorously assess the performance of our pipeline, we compiled a dataset of approximately 5,000+ images captured under varying traffic and illumination conditions in South Korea. Images were recorded using a standard monocular camera mounted on a test vehicle at three distinct times: 10 a.m., 1 p.m., and 5 p.m. This sampling strategy covers scenarios with high sun angles, partially overcast skies, and early-evening shadows. All experiments were conducted on a standard desktop (Intel(R) Core(TM) i7-9700 CPU @ 3.00GHz, 32GB RAM), demonstrating that the proposed method is computationally feasible without dedicated GPU infrastructure.

## 4.1 Implementation Details and Parameters

Key parameters of the pipeline include:

* **Bilateral Filter Kernel Size:** Selected between 5 and 9 pixels to balance noise reduction with edge preservation.

* **Morphological Dilation Size:** Typically set between 3×3 and 5×5, ensuring that dashed or slightly faded markings merge into continuous contours.

* **Minimum Polygon Area:** Set to 100 pixels to filter out minor noise or artifacts.

* **Probabilistic Hough Transform Settings:** A minimum line length of 30 pixels and a gap threshold of 5 pixels facilitate the detection of partial or dashed lines.

## 4.2 Quantitative Evaluation

Manual annotation was performed on a subset of 1,000 images to validate detection and quality labeling. Human evaluators rated each detected marking as visually good, borderline, or visually poor and used as ground truth evaluations in our work.

Throughout the experiments, it was observed that the proposed bilateral filtering plus morphological approach was able to effectively isolate lane edges in the presence of moderate wear or dashed patterns, even when shaded occlusion was available. The contrast ratio reliably distinguished freshly painted (“Good”) markings from heavily faded or unclear (“Bad”) ones, with borderline scenarios marked as “Ambiguous” for further inspection. Unlike ML-based approaches, extensive annotated data sets or specialized reflectometry equipment were not needed.
<figure><img src="image_64.png" /><figcaption>Figure 5: Some of the representative results of our proposed lane marking detection and quality assessment algorithm. The top row shows the correct detection and analysis of road markings even when partially shadowed regions exist; the second row depicts the distinction between “Bad” and “Ambiguous” quality assessment; the third row shows the successful detection under overcast/evening conditions; the bottom row depicts the robust performance of our method even in bad and dim lighting conditions.</figcaption></figure>

The pipeline’s classifications were compared with these ground-truth labels:

* **Detection Rate:** Over 90% of clearly visible lane markings were successfully localized, with most missed cases occurring under severe occlusions or extreme fading (since lane marking candidate detection was extremely difficult in such regions).

* **Quality Label Accuracy:** Approximately 86% of the classifications matched the human-labeled categories (i.e., *Good*, *Ambiguous*, *Bad*), indicating a strong correlation between our luminance-based metrics and human perception.

Figure 5 illustrates some of the representative outcomes. In many cases, even faint or partially occluded markings were detected due to the effective use of bilateral filtering and morphological operations. The contrast ratio (CR) reliably differentiated between newly painted and heavily worn markings, while the binary ratio (BR) validated the overall whiteness of the candidate regions. Markings labeled as “Ambiguous” typically exhibited moderate wear or incomplete

paint coverage, reflecting the inherent challenges in achieving consistent classification.

## 4.3 Performance and Timing

On our CPU-based system, the pipeline processed 1936×1464 resolution images at an average rate of 8~12 frames per second (fps). While further optimization (e.g., code-level improvements, vectorization, or parallelization using OpenMP) could enhance this performance, the current throughput indicates that near-real-time operation is feasible on standard hardware.

# 5 CHALLENGES AND FUTURE DIRECTIONS

Despite promising results, the proposed pipeline faces several limitations and opportunities for further improvement:
1. **Nighttime Glare and Harsh Lighting:** Although the system performs well under partially shadowed and overcast conditions, nighttime scenes with strong headlight glare or streetlight reflections can introduce artifacts that impair thresholding. As for future work, we are planning to incorporate adaptive exposure techniques and specialized nighttime preprocessing.

2. **Curved Lane Detection:** The current approach primarily handles relatively straight lane segments. Incorporating polynomial and spline-fitting methods is under development to enhance detection on winding roads or at highway interchanges.

3. **Detection of Crosswalks and Road Symbols:** Road markings are not limited to lane lines; features such as crosswalks, directional arrows, and textual signs also influence safe navigation. We are currently extending the morphological and shape approximation modules to enable effective detection and classification of these additional elements.

4. **Harsh Shadows and Occlusion Robustness:** Severe shadows, such as those cast by bridges, tunnels, or dense foliage can distort CR and BR metrics by darkening both the lane markings and the background. We are developing to implement local region-based thresholding with multi-frame data fusion to improve robustness in these challenging scenarios.

5. **Temporal Smoothing and Tracking:** Processing each frame independently may result in occasional flickering detections. Incorporating temporal smoothing or short-term tracking across consecutive frames could enhance detection stability and reduce transient artifacts.

Addressing these challenges will further improve the reliability and extend the applicability of our pipeline, from rural two-lane roads to complex urban highways.

# 6 CONCLUSION

This paper presents a comprehensive digital image processing and luminance-based pipeline for detecting lane marking candidates and assessing their quality, which avoids the complexities associated with deep/machine learning models and the necessity for specialized reflectivity sensors. By using advanced image processing techniques such as bilateral filtering, thresholding, morphological filtering, and

Hough-based line refinement, the system manages to effectively identify the regions of lane marking candidates. It then employs a dual-metric approach, utilizing contrast ratio (CR) and binary ratio (BR), to categorize each candidate as "Good," "Bad," or "Ambiguous."

Experimental evaluations conducted on a diverse dataset from South Korea demonstrate that the proposed method aligns well with human assessments of lane marking quality and performs efficiently on standard computing hardware. This solution offers a cost-effective alternative to sophisticated deep/machine learning methods and experiments that depend on using expensive reflectometers, making it particularly suitable for resource-constrained environments.

From the perspective of GIS systems, integrating techniques that detect and assess lane markings based on luminance allows to evolve from traditional static mapping frameworks to advanced, real-time monitoring systems. This advancement enhances the precision and reliability of road network databases, supports proactive road maintenance strategies, boosts the effectiveness of navigation systems, and lays a solid foundation for more informed and strategic urban planning decisions.

In future work, we plan to address nighttime-specific challenges, improve curved lane detection, and incorporate advanced shadow compensation strategies, thereby broadening the system's applicability in many GIS and ADAS contexts.

# ACKNOWLEDGMENTS

This study was conducted with the support of the "Road Traffic Infrastructure Monitoring and Emergency Recovery Support Service Technology Development (RS-2021-KA160853)" project of the Ministry of Land, Infrastructure and Transport.

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# Person Detection from UAV Based on a Dual Transformer Approach

Andrei-Stelian Stan, Dan Popescu<sup>a</sup> and Loretta Ichim<sup>b</sup>

National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania
andrei_stelian.stan@stud.etti.upb.ro, {dan.popescu, loretta.ichim}@upb.ro

**Keywords:** Neural Networks, Person Detection, Unmanned Aerial Vehicles, Detection Transformer, Vision Transformer.

**Abstract:** The study introduces a novel object detection system that combines the strengths of two advanced deep learning models, the Detection Transformer (DETR) and the Vision Transformer (ViT), to enhance detection accuracy and robustness in unmanned aerial vehicle (UAV) applications. Both models were independently fine-tuned on the VisDrone dataset and then deployed in parallel, each processing the same input to leverage their advantages. DETR provides precise localization capabilities, particularly effective in crowded urban settings. At the same time, ViT excels at identifying objects at various scales and under partial occlusions, which is crucial for distant object detection. The fusion of their outputs is managed through a dynamic fusion algorithm, which adjusts the confidence scores based on contextual analysis and the characteristics of detected objects, resulting in a combined detection system that outperforms the individual models. The fused model significantly improved overall accuracy, achieving up to 90%, with a mean Average Precision (mAP50) of 85%, and a recall of 80%. These results underline the potential of integrating multiple transformer-based models to handle the complexities of UAV-based detection tasks, offering a robust solution that adapts to diverse operational scenarios and environmental conditions.

## 1 INTRODUCTION

In the rapidly advancing field of artificial intelligence, neural network implementation for real-time object detection from unmanned aerial vehicles (UAVs) has emerged as a crucial area for academic, research, and industrial applications. UAVs, with the remarkable ability to access remote or challenging environments, present unprecedented opportunities across a spectrum of activities including urban surveillance, search and rescue missions, traffic monitoring, and environmental studies. These applications not only extend the capabilities of human operators by providing aerial insights but also enhance safety and operational efficiency, especially in scenarios where human presence could be hazardous or impractical (Wu et al. 2021). However, using UAVs for sensitive and dynamic tasks introduces complex challenges that standard object detection systems, designed primarily for static or terrestrial environments, struggle to handle. The factors complicating UAV-based person detection and tracking include high mobility, variable altitudes,

and the vast range of lighting and weather conditions under which these drones must operate. Furthermore, the UAVs' rapid movement and the diverse angles of image capture add additional layers of complexity, requiring detection systems that are accurate, exceptionally robust, and adaptable to swift changes in the visual field.

To address these challenges, the present paper introduces a novel approach that harnesses the capabilities of two transformer-based neural network architectures: the Detection Transformer (DETR) (Huang and Li. 2024) and the Vision Transformer (ViT) (Wang and Tien, 2023). DETR revolutionizes object detection by utilizing a transformer-based set prediction mechanism that eliminates the need for the complex pipelines typical of conventional detection systems. This model simplifies the learning process and enhances the efficiency of detecting objects in real-time, a critical requirement for UAV operations.

Conversely, ViT adapts the transformer architecture - previously successful in the natural language processing domain - to image analysis. ViT treats an image as a sequence of patches and processes it through self-attention mechanisms,

<sup>a</sup> https://orcid.org/0000-0002-1883-0091

<sup>b</sup> https://orcid.org/0000-0002-7465-3958
allowing the model to capture intricate dependencies
across the entire image. This capability is particularly
beneficial for UAV imagery, where objects of interest
may appear at various scales and in partial occlusions,
often against highly cluttered backgrounds.

This paper's contribution lies in the strategic
combination of these two powerful models. By
deploying DETR and ViT in parallel, each model
processes the same input independently, thus
leveraging DETR's acute precision in localization
and ViT's adeptness at handling scale variations and
occlusions. This dual-model approach mitigates the
limitations inherent in each model when used alone
and capitalizes on their complementary strengths.

A dynamic fusion algorithm orchestrates the integration of outputs from both models. This algorithm does not merely aggregate confidence scores but also intelligently adjusts the fusion ratio in real-time, based on the contextual nuances and specific characteristics of detected objects. Such a sophisticated approach ensures that the system adapts continuously to complex and evolving landscapes of UAV operation, thereby enhancing detection accuracy and robustness across a wide range of operational scenarios. This fusion of DETR and ViT sets new standards in UAV-based surveillance and monitoring, promising substantial improvements in the reliability and effectiveness of such systems. The anticipated impact of this study spans improvements in operational safety, particularly in search and rescue missions, enhancements in surveillance accuracy for security applications, and greater data precision for environmental monitoring. This approach represents a significant technological leap in computer vision and heralds a paradigm shift in how UAVs can be utilized in complex and critical applications worldwide.

## 2 RELATED WORKS

A comprehensive benchmark of real-time object
detection models tailored for UAV applications was
presented by (Du et al., 2019). The authors developed
new motion models to enhance detection accuracy in
high-speed aerial scenarios, addressing challenges
with rapidly moving objects. Their research
highlighted the importance of integrating dynamic
movement models into detection frameworks to
improve response times and accuracy in UAV-
captured imagery.

The Vision Transformer architecture was
extended by (Wang and Tien, 2023) to better suit
aerial image analysis by incorporating dynamic

position embeddings. This adaptation allows the
model to handle varying scales and orientations of
objects typically found in UAV datasets. Their
findings demonstrate significant improvements in
object detection performance on aerial images,
supporting the concept of transformers' adaptability
to specialized tasks.

(Huang and Li. 2024) introduced enhancements in
small object detection, focusing on information
augmentation and adaptive feature fusion to improve
detection accuracy and real-time performance. Their
results demonstrate superior performance over the
latest DETR model. This research is pertinent to our
work as it highlights the effectiveness of advanced
algorithms in refining object detection, echoing our
approach to optimizing UAV-based detection with
transformer architectures.

(Ye et al., 2023) introduced RTD-Net, tailored for
UAV-based object detection. It addresses challenges
like small and occluded object detection and the need
for real-time performance. By implementing a
Feature Fusion Module (FFM) and a Convolutional
Multiheaded Self-Attention (CMHSA) mechanism,
the network achieved improvements in handling
complex detection scenarios, resulting in an 86.4%
mAP on their UAV dataset. Their approach,
emphasizing efficiency and effectiveness, aligns with
our methods of optimizing object detection through
advanced architecture fusion.

## 3 MATERIALS AND METHODS

### 3.1 Dataset Used

The VisDrone dataset, which contains diverse aerial
images from various urban and rural scenes across
Asia, was used in this study. Initially, the dataset
included many objects, such as cars, buildings, and
trees. The following steps were performed to tailor it
to research needs.

Data Curation and Labeling were performed using
custom Python scripts and LabelMG. The dataset was
filtered to retain only images containing people. The
annotations were re-labeled to ensure uniformity,
combining labels for "person" and "people" into a
single "person" label.

A format conversion was performed while
preprocessing the dataset and researching ViT and
DETR accepted formats. Originally in COCO format,
the dataset was converted to Pascal VOC format. This
involved adapting the annotations and restructuring
the dataset files using a custom Python script.
The dataset was diversified having shots with persons taken from multiple angles and in different weather and light conditions such as during the night or when the sky is clouded, and the light is down (Figure 1).

Additionally, to enhance the robustness of the model, various augmentation techniques were applied to the original image (Figure 2a), such as blur (Figure 2b), (Figure 2c) decreased image brightness, and noise addition (Figure 2d). This process increased the dataset to over 2400 images, which were then split into training (70%), validation (15%), and testing (15%).

<figure><img src="image_65.png" /><figcaption>Figure 1: Examples of images from the VisDrone dataset. a) Persons in the city at night, b) Persons in a theme park from an angle on a cloudy day, c) Persons on a basketball court, d) A person driving a tuc-tuc in fog.</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>Figure 2: Examples of augmented images. a) Original image, b) Blurred image with 1.25px blur coefficient, c) Decreased image brightness by 15% d) Image with 4% noise coefficient.</figcaption></figure>

Therefore, the size of the input images and the augmentation methods used (Table 1) created a dataset with images that contain more elements, and

the edges of the objects are not as well defined as they were in the dataset used for YOLOv5 in (Stan et al. 2023).

<table><caption>Table 1: Augmentation methods, description, and values.</caption><thead><tr><td>Augmentation Method</td><td>Description</td><td>Value</td></tr></thead><tbody><tr><td>Image blur</td><td>Blur the image</td><td>2 pixels</td></tr><tr><td>Image noise</td><td>Modified the image to add noise to a percentage of the pixels</td><td>7% of pixels</td></tr></tbody></table>

## 3.2 Neural Networks Used

This sub-section explores the innovative use of neural networks, specifically focusing on the Detection Transformer (DETR) and Vision Transformer (ViT), for person detection and tracking from unmanned aerial vehicles (UAVs). These architectures leverage the power of transformers to enhance object detection tasks by simplifying the detection pipeline and enabling a more refined focus on small, distant objects typical in UAV imagery. These models improved in detecting and tracking persons in challenging UAV-captured scenes through architectural adjustments and fine-tuning relevant datasets.

DETR represents a novel approach to object detection, leveraging transformers for end-to-end object detection (Zeng et al., 2021). The implementation of Detection Transformer (DETR) for UAV-based person detection involves several steps aimed at leveraging its architecture for efficient and accurate bounding box predictions:

DETR utilizes a convolutional backbone (typically a ResNet-50 or ResNet-101) to extract feature maps from the input image. For UAV images, which often contain small and distant objects, the backbone is fine-tuned to improve its spatial resolution by adjusting the stride and kernel sizes, thus capturing finer details in the feature maps.

The transformer in DETR, which processes the outputs of the backbone, is configured to handle larger sequences of object queries. This is crucial for UAV imagery where multiple small objects might be present in a single frame. The number of object queries has increased, and the transformer is trained to focus on higher potential objects per image.

DETR simplifies the traditional object detection pipeline by eliminating the need for many hand-engineered components. It uses a set prediction loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture to
perform object detection as a direct set prediction problem.

The architecture consists of:

* Backbone: A convolutional neural network (ResNet) extracts feature representations from the input images;

* Transformer Encoder-Decoder: This core component processes the feature maps and performs object detection;

* Prediction Heads: These components predict bounding boxes and class labels for each detected object.

ViT segments an image into fixed-size patches linearly embeds each and then processes the sequence of embeddings using a standard transformer encoder (Zhang 2023). This method allows ViT to consider the entire image, unlike CNNs which process parts of an image in isolation. Architecture benefits from deeper attention layers providing the ability to focus on intricate details from complex backgrounds—common in UAV imagery.

When trained on large datasets like ImageNet-21k, ViT demonstrates superior performance in classification tasks. For object detection tasks specific to UAVs, ViT was fine-tuned on the VisDrone dataset, achieving a baseline accuracy of 72%, a recall of 0.70, and a mAP50 of 0.64.

## 3.3 Methodology

Google Colab was used for model training due to its cost-effective access to powerful GPUs and ease of setup. An A100 High RAM was utilized, having access to high-performance NVIDIA A100 GPUs. The Colab environment was configured with the necessary dependencies, including Python libraries such as PyTorch. Pre-trained COCO weights were loaded into the DETR and ViT models. This time the dataset was stored in Roboflow to simplify access to it and reduce the complexity of storing it. Annotation files were created to initialize the dataset loaders and load the model. For the training configuration parameters, we chose to test it with 30, 50, 70, 120, and 150 epochs. The learning rate optimized (Adam) dynamically adjusted the learning rate during the training.

One of the key innovations of DETR is its use of a bipartite matching loss, which directly matches predicted and ground truth objects. For UAV applications, the loss function is adjusted to be more sensitive to smaller objects by modifying the balance between the classification loss and the bounding box loss, placing more emphasis on the latter.

While DETR inherently reduces the need for Non-Maximum Suppression (NMS) through its set prediction mechanism, slight modifications are made to its post-processing NMS to better overlapping detections common in dense urban environments captured by UAVs. This involves tuning the IoU thresholds and the scoring system to finalize the detections.

DETR was trained using a mixed precision training regime to expedite the training process without losing the accuracy essential for real-time UAV operations. The training data is augmented with aerial-specific variations like varying scales, rotations, and lighting conditions to robustly train the model against the diverse conditions expected in deployment.

By refining these aspects of DETR, the model is better suited to the unique challenges posed by UAV-based detection tasks, especially in complex and cluttered environments.

The methodology centres on dual-model deployment where DETR and ViT operate in parallel. Each model processes the same input independently, allowing them to leverage their respective strengths:

* DETR excels in precise localization and good performance in crowded scenes (Song et al., 2021).

* ViT obtained good results in identifying distant or partially obscured objects due to its global processing capabilities (Song et al., 2021).

For the fine-tuning of ViT, adjustments are specifically made to its transformer's attention heads to enhance its ability to focus on small and distant objects in UAV imagery, which often appear as minute details within larger contexts:

The self-attention mechanism in ViT, which allows the model to weigh the importance of different parts of the input image, is fine-tuned to enhance its sensitivity to smaller patches. This is achieved by adjusting the attention head parameters to increase the model's focus on areas of the image that contain less information but might be crucial for identifying distant objects.

The size of the image patches that the ViT processes is reduced. Smaller patches mean the model processes more patches per image, increasing the granularity of the attention and allowing the model to focus more accurately on small objects.

The transformer encoder within ViT is enhanced with additional layers. These layers allow for the learning of more complex representations and relationships between patches, which is particularly
beneficial for identifying objects at various scales and distances.

A dynamic fusion mechanism is employed to integrate detection results. This fusion is not merely a weighted average, but an adaptive process based on scene context and object characteristics. The fusion factor is initially set at 60% but can vary depending on object size, density, and environmental conditions.

The fusion mechanism is a critical component of the methodology. It is designed to effectively combine the strengths of DETR and ViT in detecting and tracking objects—specifically, people—from UAVs. The mechanism operates on the principle that while each model has its strengths, their combined insights can provide a more accurate and robust detection system, particularly in complex environments. The fusion strategy and all the phases used are explained below.

DETR and ViT receive the same input image concurrently and process it independently. This parallel processing ensures that each model applies its unique analytical approach to the same scene.

Each model outputs a set of bounding boxes with confidence scores for each detected object. DETR, which excels in precise bounding box predictions and handling overlapping objects, provides highly accurate localization. ViT, known for its ability to recognize objects across different scales and partial occlusions, offers robustness against challenging detections.

The confidence scores from each model are first normalized and then weighted by a pre-determined fusion factor. This factor is dynamically adjusted based on validation results for the better under specific conditions (e.g., DETR for closer objects and ViT for distant objects).

The fusion algorithm evaluates the spatial overlap (using Intersection over Union, IoU) and semantic agreement of the detected objects from both models. A higher agreement in both spatial and semantic terms increase the confidence in the combined detection. The way the final decision rule is configured for the final detection and classification is described below.

The first step is combining detections. The algorithm calculates a combined confidence score for each detected object, based on the weighted scores from both models. If the detections from both models for the same object have high spatial and semantic agreement, the combined confidence score is adjusted upwards.

The combined detections are then filtered through a thresholding mechanism where only detections with a combined confidence score exceeding a set

threshold (85%) are retained. This helps to reduce false positive errors and ensures that only the most reliable detections are considered.

A modified NMS is applied to the final set of combined detections to refine the detection results. (Bolda et al., 2017). This step ensures if multiple bounding boxes are predicted for the same object, only the bounding box with the highest combined confidence score is retained.

The final output is a set of bounding boxes and associated confidence scores that represent the detected objects. Each bounding box is associated with a single object class, in this case, 'person', and reflects a higher detection accuracy than individual outputs of DETR and ViT.

This fusion mechanism not only leverages the individual strengths of each model but also introduces a robust method to adjudicate between their predictions, resulting in a more accurate and reliable object detection system for UAV applications. This approach is particularly effective in environments with diverse object scales and occlusions, typical of urban and crowded scenes.

The metrics used to evaluate the model performances are Precision, Recall, and Mean Average Precision at an Intersection over a Union threshold of 0.5 (mAP50). They are presented in Table 2 where TP = True Positives, FP = False Positives, FN = False Negatives, P = Precision, R = Recall, and mAP = Mean Average Precision. mAP50 calculates the mAP value for an Intersection Over Union (IoU) threshold of 0.5. It measures how well the model detects objects at this specific IoU threshold, indicating the proportion of correctly identified objects.

<table><caption>Table 2: Metrics used to evaluate the model.</caption><tr><td>P = <sup>TP</sup>&frasl;<sub>TP + FP</sub></td><td>R = <sup>TP</sup>&frasl;<sub>TP + FN</sub></td></tr><tr><td>AP = &int;<sub>0</sub><sup>1</sup> P(R)dR</td><td>mAP = mean (AP)</td></tr></table>

The training time varied from ~25 minutes on a 30 epochs range up to ~82 minutes with a 150 epochs range. However, it can be considered faster than the time recorded with YOLOv8 which resulted in ~72 minutes for 120 epochs.

## 3.4 System Implementation

DETR was initialized with pre-trained weights on the COCO dataset. This approach ensures a valid starting
point since COCO is large and diverse enough to
leverage rich feature representations.

The pre-trained weights were loaded into the DETR architecture, focusing on adapting the final layers to our specific task of person detection.

The training configuration included specifying hyperparameters such as image size (640×640), batch size (16), learning rate, and number of epochs (30, 50, 70, 120, 150). The model was trained using the Adam optimizer, known for its adaptive learning rate capabilities, which helps to achieve faster convergence. After each training session (30, 50, 70, 120, and 150 epochs), the model was evaluated on the validation set to assess its performance.

The final trained model was tested on the holdout test set to obtain unbiased performance metrics.

The implementation of the fusion mechanism went through multiple phases (Figure 3) and is described in more detail below. Fusion Mechanism Implementation based on previously fine-tuned DETR and ViT.

* **Confidence Score Calculation:** After receiving detection outputs (bounding boxes and confidence scores) from both models, these scores are normalized.

* **Weight Assignment:** Implement the adaptive weighting system where each model's confidence score is multiplied by a predetermined but adjustable fusion factor. This factor might be dynamically tuned based on ongoing performance assessments or environmental context.

* **Spatial and Semantic Analysis:** Calculate the IoU for bounding boxes that overlap across models. Combine boxes with high IoU and similar class labels by averaging their positions weighted by their confidence scores.

* **Application of Modified NMS:** Apply a version of NMS that considers the fusion confidence scores to resolve conflicts between overlapping boxes, ensuring that each object is detected only once with the highest possible accuracy.

The following parallel Processing Pipeline steps have been used:

* **Input Handling:** Configure an input pipeline that preprocesses images from UAV cameras to match the input requirements of both models (e.g., resizing, normalization).

* **Concurrent Model Invocation:** Deploy both models synchronized to process the same input simultaneously. Use threading or asynchronous programming to manage parallel execution without bottlenecks.

Inference and Real-time Processing details:

* **Batch Processing vs. Streaming:** Depending on the application, implement the system to handle batch processing of collected images or real-time streaming of video feeds. In this case, processing batch images was tested.

* **Decision Making:** Based on combined confidence scores and the results of the NMS, finalize the detection output. This output includes the class (person), the location (bounding box), and the detection confidence.

Feedback and Continuous Learning details:

* The feedback mechanism allows the system's output to be periodically reviewed by human supervisors to tag inaccuracies.

* Using this feedback the fusion factor is adjusted as needed and refinement of the model parameters during scheduled re-training sessions, enhancing the system's accuracy and adaptability over time.

<figure><img src="image_67.png" /><figcaption>Figure 3: Global model architecture.</figcaption></figure>
# 4 EXPERIMENTAL RESULTS AND DISCUSSIONS

The effectiveness of the combined DETR and ViT model was evaluated through a series of experiments using the VisDrone dataset, which consists of diverse urban and rural scenes captured via UAVs. The models were independently fine-tuned on this dataset before being deployed in parallel.

The paragraphs below describe the individual models' performance.

DETR achieved an accuracy of 84%, a mean Average Precision (mAP50) of 78%, and a recall of 73% (Table 3). DETR excelled particularly in densely populated urban scenes where precise localization of multiple objects is critical.

<table><caption>Table 3: Metrics obtained after validation.</caption><thead><tr><td>Metric</td><td>DETR</td><td>ViT</td><td>Fusion</td></tr></thead><tbody><tr><td>Precision</td><td>0.842</td><td>0.718</td><td>0.889</td></tr><tr><td>Recall</td><td>0.73</td><td>0.701</td><td>0.782</td></tr><tr><td>mAP50</td><td>0.78</td><td>0.643</td><td>0.826</td></tr></tbody></table>

ViT demonstrated an accuracy of 72%, a recall of 70%, and a mAP50 of 64%. Its performance was notably superior in detecting smaller and more distant objects, which models often miss because they rely heavily on localized contextual information.

The paragraphs below describe the combined model performance. The fusion mechanism implementation significantly enhanced overall performance, resulting in an accuracy of 90%, a mAP50 of 85%, and a recall of 80%. The improvement was particularly notable in complex environments where individual models showed limitations:

In urban settings characterized by high object density and diverse object scales, the combined model improved detection accuracy by up to ~15% on average compared to individual models.

In adverse weather conditions, which typically impede visual clarity and object recognition (mostly during the night with low or very low light and during daylight within dusty areas), the fused model demonstrated resilience, maintaining high accuracy rates that surpassed each model operating independently by a significant margin.

The results in Figure 4 underline the potential of integrating multiple transformer-based models to handle the complexities of UAV-based object detection.

The dynamic fusion approach not only leveraged the unique strengths of DETR and ViT but also

facilitated a robust performance across varied and challenging environments, showcasing the practical implications of this research in real-world applications.

Our proposed method combines the strengths of both DETR and ViT through a fusion mechanism that leverages their complementary features. By running both models in parallel and integrating their outputs using adaptive weighting and modified NMS, our approach addresses the shortcomings of individual models. The fusion mechanism enhances the detection of small and distant people while maintaining high precision and recall rates.

<figure><img src="image_68.png" /><figcaption>Figure 4: Examples of experimental results from the fusion model, a), c), e) - test images, b), d), f) - processed images.</figcaption></figure>

Compared to traditional CNN-based detectors, the proposed fusion model demonstrates better performance in terms of precision, recall, and mAP50 metrics, as shown in Table 3. The adaptive weighting system allows the model to dynamically adjust to varying environmental conditions and object scales, which is not commonly addressed in other studies.

Moreover, while transformer-based models are still emerging in UAV imagery analysis, our approach showcases their potential when effectively combined. The fusion of DETR and ViT improves detection accuracy and maintains computational efficiency for real-time applications.
Our method distinguishes itself from existing ones by integrating feedback and continuous learning mechanisms. By incorporating human-in-the-loop feedback to adjust the fusion factors and refine model parameters, the system adapts over time, enhancing its robustness and applicability in diverse operational scenarios.

Our implementation offers a novel solution that outperforms existing methods by effectively addressing the unique challenges of person detection and tracking in UAV imagery. Combining advanced transformer architectures with an adaptive fusion mechanism presents a significant step forward in developing reliable and efficient UAV-based detection systems.

# 5 CONCLUSIONS

DETR and ViT integration through the described fusion mechanism has proven a promising solution for enhancing object detection capabilities in UAV operations. This study highlights the complementary strengths of the two transformer models and paves the way for research in future papers on multi-model fusion strategies. Future studies may explore adaptive algorithms that could refine the fusion process based on continuous learning from diverse environmental data and real-time operational feedback.

# REFERENCES

Bodla, N., Singh, B., Chellappa, R., Davis, L. (2017). Improving object detection with one line of code, 2017 IEEE International Conference on Computer Vision (ICCV).

Du, D. et al. (2020). VisDrone: The vision meets drone object detection in image, IEEE/CVF International Conference on Computer Vision (ICCV).

Huang, J., Tianrui Li, T. (2024). Small object detection by DETR via information augmentation and adaptive feature fusion. Proceedings of 2024 ACM ICMR Workshop on Multimodal Video Retrieval.

Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B. (2022). A review of Yolo algorithm developments. Procedia Computer Science, vol. 199, 2022, pp. 1066-1073.

Song, H., Sun, D., Chun, S., Jampani, V., Han, D., Heo, B., Kim, W., Yang, M.H. (2021). ViDT: An efficient and effective fully transformer-based object detector, 2021, arXiv:2110.03921.

Stan, A.S., Ichim, L., Parvu, V.P., Popescu, D. (2023). Person detection and tracking using UAV and neural networks. 31st Mediterranean Conference on Control and Automation (MED), Limassol, Cyprus.

Wang, L., Tien, A. (2023). Vision transformer with dynamic position embeddings for object detection in aerial images. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Pasadena, CA, USA.

Wu, X., Li, W., Hong, D., Tao, R., Qian Du, Q. (2021). Deep learning for UAV-based object detection and tracking: a survey. IEEE Geoscience and Remote Sensing Magazine.

Ye, T., Qin, W., Zhao, Z., Gao, X., Deng, X., Yu Ouyang, Y. (2023). Real-time object detection network in UAV-vision based on CNN and Transformer, IEEE Transactions on Instrumentation and Measurement.

Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., Wei, Y. (2022). MOTR: End-to-end multiple-object tracking with Transformer. Proceedings 17th European Conference (ECCV), Part XXVII.

Zhang, J. (2023). Towards a high-performance object detector: insights from drone detection using ViT and CNN-based deep learning models, 2023 International Conference on Computer Vision and Robotics Science.
# Analyzing Multitemporal Datasets to Monitor Topographic Changes in Rio Cucco Italy

Jad Ghantous<sup>a</sup>, Vincenzo Di Pietra<sup>b</sup>, Elena Belcore<sup>c</sup> and Nives Grasso<sup>d</sup>

DIATI, Politecnico di Torino, C.so Duca Degli Abruzzi, Turin, Italy
{jad.ghantous, vincenzo.dipietra, elena.belcore, nives.grasso}@polito.it

**Keywords:** Natural Hazards, Digital Terrain Model, Point Cloud, Semantic Segmentation, Deep Learning.

**Abstract:** Rio Cucco is an Italian catchment located in Malborghetto of Friuli Venezia Giulia. It is considered an area of interest regarding its hydrological and morphological properties. The area has historically been affected by natural hazards such as rockfall and landslides, mainly related to extreme rainfall events like the 2003 storm that affected the Fella river or the Vaia storm of October 2018. These events highlight the importance of understanding the morphological and topographic modification of the area also in relation to the realization of protection and hydraulic works. The changes in Rio Cucco were documented by comparing open-source historical data with ad-hoc UAV surveys focusing the analysis on 3D products like point clouds and the digital terrain models. The source of the recent data was an Aerial LiDAR- based survey conducted by our team in June 2024 while the historical data was taken from the FVG region's geoportal and referred to 2017. After comparing the different datasets with traditional techniques like nearest neighbour Euclidean distance or DEM of Difference, changes were evident pointing to potential rockfalls between the year 2024 and 2017. A deep learning model was explored and in development for the semantic segmentation of the area.

## 1 INTRODUCTION

Natural hazards are influenced by climatic conditions. Climate change affects the frequency and intensity of hazards such as landslides by modifying the hydrological regimes (Schneiderbauer et al., 2021). In a recent study by Semnani et al. (2025), landslide susceptibility was modeled across California based on different climate scenarios. They found that there was an overall increase in susceptibility in respect to time until the year 2100 in areas such as Sierra Nevada and the areas near the coast of California. Several areas have experienced landslides over the years. A landslide in Mount Meager Canada occurred in 2010 triggered by three decades of temperature increases that degraded the permafrost (Huggel et al., 2012). In the case of Italy, Monte Rosa was affected by a landslide in 2007 as a result of permafrost degradation as well (Huggel et al., 2012). Another example, is the Esino River basin in central Italy which was studied based on the relationship between climate change and

landslides (Sangelantoni et al., 2018). The frequent landslides prompted the creation of several research projects to study and monitor the relationship between the areas at risk of natural hazards and their geomorphological properties. Among these projects, PRIN funded MORPHEUS (GeoMORPHoMetry through Scales for a resilient landscape), which this case study was part of, aims to involve several research bodies to collaborate on the relationship between natural hazards and the sediment connectivity of the area. Sediment connectivity refers to the degree of linkage that controls sediment fluxes through a landscape, especially between the sediment source and the downstream area (Cavalli et al., 2013). Sediment connectivity is especially important in mountainous catchments. The presence of natural and anthropic structures (roads, terraces, dams) greatly affects the connectivity pathways. This as a result impacts the estimation of natural hazards. Therefore, morphological variations derived from multi-temporal topographic information are fundamental to

<footnote>a https://orcid.org/0009-0001-3817-3578</footnote>

<footnote>b https://orcid.org/0000-0001-7501-1183</footnote>

<footnote>c https://orcid.org/0000-0002-3592-9384</footnote>

<footnote>d https://orcid.org/0000-0002-9548-6765</footnote>
implement functional sediment connectivity analysis. In this context, a UAV survey represents the main source of high temporal resolution topographic data allowing the generation of several 3D products from LiDAR or Photogrammetric processing. Among these products, Digital Elevation Models are fundamental to derive structural connectivity parameters while DEMs of Difference are used to estimate the impact of topographic changes. Therefore, a multi-temporal analysis is crucial to successfully achieve the goal of the Morpheus project. In the case of this project, the regions particularly studied in the framework of sediment connectivity include Liguria, Veneto and Friuli Venezia Giulia. The selected study areas encompass several catchments located in contrasting landscapes of northern Italy and featuring different sediment transport processes. Among the many, Rio Cucco basin (0.65 Km²) saw an extreme event in 2003 that triggered an unusually large debris flow in the area.

In order to test the multi-source, multi-scale and multi-temporal coherence of spatial heterogeneous data, fundamental for assessing the impact of topographic changes, a UAV- based survey of Rio Cucco was conducted by our team in June 2024. The presence of a natural pine forest over much of the basin surface directed us toward the choice of a LiDAR sensor for dense point cloud acquisition and DEM production. After post-processing and analysis of these datasets, a comparison was needed with the same type of data provided by EAGLE FVG (official geo portal of Friuli Venezia Giulia). The aim from this comparison is to document the temporal changes occurring in Rio Cucco and to harmonize different datasets referred to the same study area. To this purpose, the data will be analysed and validated in terms of point clouds density, georeferencing, accuracy, and uncertainties by exploiting data co-registration algorithms. Furthermore, to assure the optimal output for the geomorphometric approach, a land cover classification procedure was tested relying on a simple deep learning model.

## 2 STUDY AREA

The Rio Cucco basin is a torrential watercourse located in the municipality of Malborghetto-Valbruna (Udine, Italy) where it serves as a right tributary of the Fella River. Its watershed comprises two sub-basins, fed by smaller channels and two main branches that converge into an alluvial fan near the village of Cucco. The basin's geology features dolomites and dolomitic limestones from the Anisian Ladinian period

(Dolomia dello Sciliar), along with nodular limestones, marls, and calcarenites in its upper areas. The southern slopes of Monte Cucco are marked by vertical rocky cliffs, frequent rockfalls, and debris accumulations, particularly in regions dominated by friable calcareous and dolomitic rocks prone to thermoclastism and cryoclastism.

The alluvial fan, spanning roughly 0.2 km², hosts a natural pine forest of black pine and Scots pine in its apex and residential areas, meadows, and a state road bridge at its lower end. The predominantly south-facing aspect, combined with arid conditions caused by the area's permeability and frequent material deposition, inhibits soil formation. Annual precipitation averages 1,500 mm, with summer and autumn peaks corresponding to increased debris flow activity. Significant flood events, such as those in June 1996 and August 2003, caused extensive sediment mobilization and damage, with the latter involving 325 mm of rainfall over 12 hours and the transport of approximately 80,000 m³ of sediment.

After the 2003 flood, more than 300 million euros were invested throughout the Valcanale and Canal del Ferro to secure, restore existing hydraulic works as well as build new structures on the most affected basins (FVG Region, 2007). Two branches of the basin were extensively reworked with the construction of cross works and berms, which converge to a downstream storage basin of 100,000 m³. The latter, in particular, is protected by two cyclopean boulder embankments, joined by a filtering weir that conveys the solid-liquid material to a reinforced concrete gutter near the State Road crossing, which is followed by another channelling work in cemented boulders to the point of discharge into the Fella River. The basin plays a vital role in mitigating flood risks, directing sediment-laden flows into reinforced concrete channels towards the Fella River. Figure 1 shows the location of the basin with respect to the surrounding area.

<figure><img src="image_69.png" /><figcaption>Figure 1: Rio Cucco Basin.</figcaption></figure>
# 3 METHODOLOGIES

In the case of Rio Cucco, the area was studied by comparing FVG 2017 data with the survey that was conducted in the period between 11 and 14 June 2024.

The EAGLE FVG survey occurred in 2017 thus all the data including the point clouds and digital terrain model refer to that survey. There were two aspects that were considered for the comparison. The point clouds and digital terrain models were used in the main comparison criteria. Moreover, after the comparison between the datasets, a deep learning models was explored to assure the optimal output for the geomorphometric approach, main goal of the MORPHEUS Project.

## 3.1 Point Clouds

A DJI Matrice 300 RTK drone equipped with a DJI Zenmuse L1 Lidar sensor was used to acquire the point cloud in the June 2024 survey. Consequently, DJI Terra software was used to process the data from the drone and create the point clouds of the area (DJI, n.d.). The point cloud that was output from the software was a merged file that contained all the point clouds of the area surveyed and individual point cloud segments that make up the merged point cloud. Both the merged and the individual point clouds were saved. Table 1 shows the different parameters used in the production of the point clouds with DJI Terra.

The output merged point cloud .las file was 32.6 GB in size which made the file demanding from a computational point of view. Spatial subsampling was used in CloudCompare to reduce the density and size of the point clouds to a workable size (Girard eau-Montaut, n.d.).

The point clouds were subsampled at spaces of:

* 0.50 (size: 0.4 GB)

* 0.25 (size: 1.9 GB)

* 0.20 (size: 2.3 GB)

* 0.15 (size: 3.8 GB)

* 0.10 (size: 7.1 GB)

Spatial subsampling has an inverse relationship with cloud density. A larger spatial subsampling leads to higher workability but less data. The aim was to find a workable point cloud with a point cloud density greater than 16 points/m².

<table><caption>Table 1: DJI Terra Software Parameters.</caption><thead><tr><th colspan="2">DJI Terra</th></tr><tr><th colspan="2">Point Cloud Optimization Parameters</th></tr></thead><tbody><tr><td>Point Cloud Density (by Percentage)</td><td>High</td></tr><tr><td>Point Cloud Effective Distance Range</td><td>3 – 300 m</td></tr><tr><td>Optimise Point Cloud Accuracy</td><td>Yes</td></tr><tr><td>Smooth Point Cloud</td><td>Yes</td></tr><tr><th colspan="2">Point Cloud Output Parameters</th></tr><tr><td>Ground Point Classification</td><td>Yes</td></tr><tr><td>Ground Point Classification Parameters</td><td>Ground Point Type: Steep Slope<br>Maximum Diagonal of the Building: 25 m<br>Iteration angle: 9°<br>Iteration distance: 0.6 m</td></tr><tr><td>DEM Parameters</td><td>By GSD | 0.5 m</td></tr><tr><td>Point Cloud Format</td><td>PNTS | LAS</td></tr><tr><td>Merged Output</td><td>Yes</td></tr><tr><td>LiDAR Point Cloud Block Count</td><td>9</td></tr><tr><td>Output Coordinate System</td><td>WGS 84 / UTM zone 33N | Default</td></tr></tbody></table>

The EAGLE FVG point clouds refer to the acquisition conducted in the area in 2017 and had RDN 2008 UTM 33N as the default reference system. The comparison conducted between the recent data in 2024 and the EAGLE FVG 2017 data was based on two main parameters. The surface density of the point clouds and the Cloud to Cloud distance were used as the comparison parameters of this study taking the same reference system into account.

## 3.2 Digital Terrain Models

The digital terrain model (DTM) produced using DJI Terra was based on its classification of ground points and non-ground points according to its own algorithm. The parameters used for the creation of the DTM were mentioned in Table 1(regarding the slope steepness, maximum diagonal of the building etc.).

The output DTM was a digital terrain model with removal of canopy and a resolution of 0.5 m as set in the DJI Terra. If no classification of ground points was selected, the result would be a digital surface model that has no separation between ground points and canopy. It is to be noted that the parameters were tweaked to produce the most connected DTM. Keeping the default values they resulted in a heavily tiled DTM that had empty spaces between different tiles. Moreover, the subsampled point cloud with the 0.25 spatial spacing was input into Agisoft Metashape
to produce a DTM with the same resolution of 0.5 m (Agisoft LLC, n.d.). Using Agisoft Metashape to create a digital terrain model from the produced point cloud serves as an alternative to DJI Terra to see how different algorithms behave in digital terrain model generation.

After the comparison between the digital terrain models produced using DJI Terra & Agisoft Metashape, the next step was comparing the digital terrain models to EAGLE FVG digital terrain models for the year 2017. The files on the EAGLE FVG website were in ascii format and yielded several tiles that were merged and clipped to the shape of the Rio Cucco basin. As previously conducted with the comparison of the point clouds, the 2024 DTM was converted to the reference system of the EAGLE FVG 2017 DTM (RDN 2008 UTM 33N) to create a Difference of Digital Elevation between the two digital terrain models.

## 3.3 Land Cover Classification Using a Deep Learning Model

An important task for this project is conducting land cover classification of the Rio Cucco basin so that land cover and land use can be quantified and monitored for the area. Land cover classification is particularly useful to monitor natural hazards. There have been studies regarding the usage of land cover classification in risk management. UAV very high resolution data was used in the classification of the Niger River (Belcore et al., 2022). The authors used a multitude of geomatic techniques to process the data and enrich feature extraction based on spectral, textural and elevation data acquired from the survey. This allowed for the creation of different classes necessary for the food map creation to an area that had very little coverage. Covering another work by these authors, mountainous areas in Italy studied in land cover classification using Google Earth Engine. That study addressed the challenges of accurately classifying mountainous areas due to the variability of reflectance and shadows (Belcore et al., 2020). A land cover classification is required for Rio Cucco and as a requirement of the project MORPHEUS, artificial intelligence techniques need to be explored for automatic classification of the study area. Deep learning, specifically through the use of convolutional neural networks (CNNs), offers a powerful approach to semantic segmentation of satellite imagery (Zhang et al., 2019). This involves classifying each pixel in an image into a specific category or class (e.g., building, road, forest). Consequently, an open-source semantic segmentation model was used to conserve

time as well as explore the feasibility of deep learning applications for this work. The model DeepLabV3 ResNet50 model was developed as an open-source library on Python. As this model is not particularly only made for satellite images and remote sensing, the model needed training. The model was trained with a Kaggle dataset of satellite images that contains the images alongside their masks (Kaggle, n.d.). This dataset itself is compiled from three main datasets labeled: Semantic segmentation of aerial imagery (Roia Foundation), Land Cover Classification - Bhuvan Satellite Data (Indian Space Research Organization), Urban Segmentation (International Society for Photogrammetry and Remote Sensing). The dataset contained classes shown in Table 2. These classes were used as they are and in their same color in the training and testing of the data to see the effectiveness of the model.

<table><caption>Table 2: Dataset Default Classes.</caption><thead><tr><td>Class</td><td>Color</td></tr></thead><tbody><tr><td>Building</td><td>Dark Purple</td></tr><tr><td>Land / Unpaved area</td><td>Light Purple</td></tr><tr><td>Road</td><td>Light Blue</td></tr><tr><td>Vegetation</td><td>Yellow</td></tr><tr><td>Water</td><td>Orange</td></tr><tr><td>Unlabeled</td><td>Gray</td></tr></tbody></table>

Table 3 summarizes the different parameters used in the deep learning model training.

<table><caption>Table 3: Deep Learning Model Parameters</caption><thead><tr><td>Parameter</td><td>Value</td><td>Description</td></tr></thead><tbody><tr><td>Batch Size</td><td>8</td><td>Number of images processed in parallel during training</td></tr><tr><td>Image Dimensions</td><td>256 x 256</td><td>Resized dimensions of input images</td></tr><tr><td>Number of Classes</td><td>6</td><td>Number of distinct classes</td></tr><tr><td>Model Architecture</td><td>DeepLabV3 (ResNet50)</td><td>The backbone used for feature extraction and segmentation</td></tr><tr><td>Pretrained Weights</td><td>COCO_WTTH_VOC_LABELS_V1</td><td>Weights pretrained on the COCO dataset</td></tr><tr><td>Dataset Size</td><td>203 images</td><td>Total number of image-mask pairs</td></tr><tr><td>Training Epochs</td><td>400 epochs</td><td>Total number of epochs used in training</td></tr><tr><td>Input Channels</td><td>3 (RGB)</td><td>Number of channels in the input image</td></tr><tr><td>Output Channels</td><td>6</td><td>Number of segmentation classes predicted by the model</td></tr></tbody></table>
After training the model with that dataset for more than 400 iterations, a new orthophoto was input to test the model. The orthophoto for the area was compiled by converting the subsampled 0.25 point cloud in Agisoft Metashape into create an orthophoto. With respect to the 2017 orthophoto, the data was downloaded from EAGLE FVG. Both datasets were resampled to be 0.5 m in resolution. The main objective of these orthophotos was to create testing for the deep learning model being developed for this project.

# 4 RESULTS

Comparing the different datasets for Rio Cucco, it is evident that were changes in the area from 2017 to 2024. The changes were particularly evident when the difference of digital elevation (DoD) was calculated. There were areas where there was possible sediment deposition leading to an elevation increase at the location of the deposition and an elevation decrease where the sediments disconnected. This indicates that a rockfall possibly occurred between 2017 and 2024.

## 4.1 Point Clouds

### 4.1.1 Surface Density

Based on several iterations to choose the best subsample with respect to its usability and its resolution. It was found that the 0.15 spatial subsample model was the most preferable serving a high ratio between resolution and workability. This point cloud had more than 95 % of its density greater than 16 points/m² but it was light enough to run without causing any issues. Since the point clouds produced during the June 2024 survey were larger than the basin. The selected point cloud would be then trimmed based on the boundaries of the basin and used to directly compare with the EAGLE FVG 2017 points cloud based on the same area of interest in the basin.

<table><caption>Table 4: Surface Density of Rio Cucco Point Cloud Subsamples 2024.</caption><thead><tr><th colspan="6">Surface Density (points/m²)</th></tr><tr><th>Pt<br>Cloud</th><th>μ</th><th>σ</th><th>Min</th><th>Max</th><th>% of pts ><br>16 pts/m²</th></tr></thead><tbody><tr><td>0.5</td><td>10.8</td><td>6.1</td><td>0.32</td><td>21.2</td><td>25.0</td></tr><tr><td>0.25</td><td>54.9</td><td>31.6</td><td>0.32</td><td>109.4</td><td>85.6</td></tr><tr><td>0.2</td><td>94.2</td><td>54.4</td><td>0.32</td><td>188.0</td><td>91.4</td></tr><tr><td>0.15</td><td>171.9</td><td>99.4</td><td>0.32</td><td>343.4</td><td>95.3</td></tr><tr><td>0.1</td><td>390.3</td><td>226.0</td><td>0.32</td><td>780.3</td><td>97.7</td></tr></tbody></table>

Based on the results from Table 5, both point clouds show very similar surface density with both having more than 95 % of their points being significantly higher than the required 16 points/m².

<table><caption>Table 5: Surface Density of Clipped Rio Cucco Point Cloud FVG 2017 and 0.15 Subsample 2024.</caption><thead><tr><th colspan="6">Surface Density (points/m²)</th></tr><tr><th>Pt<br>Cloud</th><th>μ</th><th>σ</th><th>Min</th><th>Max</th><th>% of pts ><br>16 pts/m²</th></tr></thead><tbody><tr><td>2017</td><td>190.1</td><td>110.0</td><td>0.32</td><td>379.9</td><td>95.7</td></tr><tr><td>2024</td><td>171.4</td><td>99.2</td><td>0.32</td><td>342.4</td><td>95.3</td></tr></tbody></table>

### 4.1.2 Cloud to Cloud Distance

Next, to accurately check the differences between the point cloud of 2017 and the one created in June 2024, the boundaries between both point clouds should be identical and the reference system should also be identical. The reference system that should be worked on is RDN2008 UTM 33N, which is the same reference system used by EAGLE FVG.

Furthermore, the Cloud-to-Cloud distance was performed before and after ICP by placing the EAGLE FVG point cloud as the reference option and the Rio Cucco 2024 0.15 Subsample point cloud as the compared option in each scenario. The Cloud to Cloud Distance helps in identifying what the differences between both point clouds were during the different time periods of the area.

Table 6 shows the differences in the Cloud to Cloud distance between the same point clouds from June 2024 saved in different formats and the 2017 EAGLE FVG point cloud.

<table><caption>Table 6: Cloud to Cloud Distance Between the 2017 EAGLE FVG Point Clouds and 0.15 Subsample 2024.</caption><thead><tr><th colspan="6">Cloud 2 Cloud Distance (m)</th></tr><tr><th>Pt<br>Cloud</th><th>μ</th><th>σ</th><th>Min</th><th>Max</th><th>% of pts<br>&lt; 1m</th></tr></thead><tbody><tr><td colspan="6">Before ICP</td></tr><tr><td>RDN</td><td>3.7</td><td>2.1</td><td>0.00</td><td>7.4</td><td>13.7</td></tr><tr><td colspan="6">After ICP (85 % Final Overlap)</td></tr><tr><td>RDN</td><td>3.6</td><td>2.1</td><td>0.00</td><td>7.2</td><td>14.1</td></tr></tbody></table>

As seen from Table 6, performing an ICP slightly reduces the nearest neighbor distance between the two point clouds. Observing that table, it is possible to notice a decrease in the mean distance of about 10 cm with the same standard deviation, which means that both EAGLE FVG data and the authors data are been produced with the same accuracy in georeferencing. It is noticeable that the mean distance between the point clouds is more than three meters.