bluuebunny/crossref_metadata_2025 huggingface dataset:
Stigma, perceived family support, and post-traumatic stress experienced by seropositive HIV patients in Pakistan
Authors: Zia Kiran , Khalid Faiza
Published: October 2022

Background and Objective: Stigmatization and discrimination are phenomena familiar to all human immunodeficiency virus (HIV)-positive patients, which create a significant amount of stress; little is known about the role of family support in alleviation of this stressful condition. The current study aims to investigate the relationship between the perceptions of family support, stigmatization, and symptoms of posttraumatic stress in HIV-positive patients in Pakistan.
Methods: A total of 100 seropositive patients, aged between 18 and 50 years, were approached through professional organizations in Lahore, Pakistan, based on purposive and snowball sampling strategies. The magnitude of stigma experienced by the patients was assessed by administering the Berger HIV stigma scale. The multidimensional scale of perceived social support and post-traumatic stress disorder checklist were used to assess post-traumatic stress symptoms (PTSS) and perceived family support (PFS).
Results: The study identified a positive relationship between experiencing stigma and the development of PTSS. Also, a significant inverse relationship between PFS and PTSS was observed, whereas a consequential negative association between experiencing stigma and PFS was evident in patients who disclosed their illness to their families.
Conclusion: The current study documents that HIV seropositive patients in Pakistan experience high levels of stigma, especially in relation to the disease  disclosure to their families and other social contacts. An already immune compromised status and stigmatization leads to significant stress symptoms which ultimately decrease the perception about accessibility of social support in the times of crisis.
Effect of anterior chamber depth on the accuracy of different intraocular lens’ formulas
Authors: Moin Muhammad , Malik Tayyaba Gul , Alam Rabail
Published: October 2022

Background and Objectives: Due to the difference in anterior segments among different races, intraocular lens formulas behave differently. Asian eyes have smaller anterior segment dimensions than Caucasian eyes. This study was carried out to evaluate the effect of different values of anterior chamber depth on the accuracy of Sanders, Retzlaff, Kraff/Theoretical (SRK/T), Hill Radial Basis Function (Hill RBF 2), and Barrett Universal II (Barrett U II) formulas.
Methods: This was a descriptive observational study. Ninety-six eyes of patients, who underwent phacoemulsification with intraocular lens implantation and ended uneventfully, were included. The patients were divided into two groups based on the anterior chamber depth (ACD). Group 1 had ACD > 3 mm and group 2 had ACD < 3 mm. Intraocular lens (IOL) power with SRK/T was calculated with a built-in formula in IOL Master 500. Barrett Universal II and Hill RBF 2 formulas were calculated using online calculators. Descriptive statistics were calculated for both groups. An independent t-test was applied for group comparison.
Results: Comparisons of the mean prediction errors of groups 1 and 2 using three different formulas were not statistically significant (p > 0.05). However, SRK/T had the lowest median prediction error for both groups but the highest percentage of eyes within ±0.5 D of absolute prediction error (APE) for group 1 and the lowest percentage of eyes within ±0.5 D of APE for group 2.
Conclusion: There was no statistically significant effect of different anterior chamber depths on the accuracy of SRK/T, Barrett U II, and Hill RBF 2. The three formulas behaved similarly with different depths of the anterior chamber.
Assessment of knowledge regarding hand hygiene among healthcare workers at a cantonment-based public sector hospital in Pakistan
Authors: Ahmed Saleh , Khan Khola Waheed , Ilyas Sabeeh , Nadeem Sadia
Published: October 2022

Background and Objective: Hand hygiene is regarded as one of the most important strategies in limiting infections and hospital-acquired diseases in healthcare settings. Adopting an optimal and scientifically validated technique of maintaining hand hygiene in healthcare centers is considered the safest and cost-effective method to prevent infection transmission to and from the patients. The present study was planned to determine the knowledge of healthcare workers about hand hygiene using the standard questionnaire in a local tertiary care hospital.
Methods: It was a cross-sectional survey conducted at the Pakistan Ordnance Factories Hospital, Wah Cantonment, Pakistan, for 3 months. By adopting purposive sampling, a total of 165 healthcare professionals were recruited for the study. “Hand Hygiene Knowledge Questionnaire for Healthcare Workers,” published by World Health Organization (WHO), was used for data collection. Each subject filled out the questionnaire by himself/herself. Data were entered and analyzed in Statistical Package for the Social Sciences version 22.0.
Results: It was observed that only 11 (6.6%) subjects had good knowledge of hand hygiene according to the standard guidelines, while majority of the respondents (58.2%) had a moderate level of knowledge according to the WHO criteria. There was no statistical difference in the scores among medical doctors and nurses.
Conclusion: The level of knowledge about hand hygiene among healthcare workers is unsatisfactory. It calls for effective reinforcement through repeated training and awareness seminars as a part of routine work to inculcate this habit for better infection control in hospital settings. 
Schizophrenia is linked to dyslipidemia with minimal alterations in the inflammatory biomarkers - an experience from Saudi Arabia
Authors: Alyoubi Razan Ahmed , Mahassni Sawsan Hassan
Published: October 2022

Background and Objective: Schizophrenia (SZ) is a chronic mental illness that is associated with increased inflammation, adverse effects on the immune system, and abnormal concentrations of biochemical molecules. This study is the first to determine the concentrations of C-reactive protein (CRP) and some  proinflammatory and anti-inflammatory cytokines, the differential complete blood count (CBC), and the lipid profile in Saudi patients with SZ.
Methods: Blood samples were collected from 45 randomly chosen male in-patients with chronic SZ and 29 healthy males, with an age range of 28-47 years. The concentrations of CRP, IL-6, IL-4, TNF-α, and IFN-γ; body mass index (BMI); CBC; and the lipid profile were determined through standard guidelines and compared between the patients and controls.
Results: The results showed that there were no significant differences in the CRP and cytokines’ concentrations for the patients compared with those of the controls. Patients, however, had significantly higher red blood cell (RBC) distribution width. Additionally, patients had significantly lower BMI, basophil and RBC counts, basophil-lymphocyte and eosinophil-lymphocyte ratios, hemoglobin, cholesterol, and low-density lipoprotein concentrations.
Conclusion: In conclusion, patients with SZ in local population showed slight inflammation and the immune system was minimally affected. Additionally, patients had dyslipidemia and were possibly more prone to anemia.
In the Memory of GR (Ghulam Rasool)(01-01-1990 to 23-09-2022)
Authors: Naseem Prof. Nadia
Published: October 2022

NA
İstanbul park et devam et sisteminin sürdürülebilir ve entegre ulaşıma etkisi
Authors: TARHAN Asena Kübra , ERCOSKUN Ozge
Published: October 2022

Günümüzde kentlerde nüfus artışı, teknolojinin gelişmesi, kentsel yayılma ve bunlara bağlı olarak motorlu taşıt kullanımı artmaktadır. Özel araç kullanımının artması ile özellikle büyük kentlerde trafik sıkışıklığı yaşanmaktadır. Bu durum beraberinde hava, gürültü kirliliği ve karbon salınımı gibi çevre sorunlarının oluşmasına neden olmaktadır. Sürdürülebilir, entegre ulaşımın sağlanmasında ve otomobil kullanımının azaltılmasında kentlerde park et devam et sistemleri uygulanmaktadır. Park et devam et (P+D), kent içi seyahatlerde özel araç kullanımını azaltmak için kentte ve merkez çeperlerinde oluşturulan otoparklar ile yolculukların toplu taşıma ile devam etmesini sağlar. Bu çalışmanın amacı, İstanbul kenti park et devam et sisteminin sürdürülebilir ve entegre ulaşım dahilinde incelenmesidir. Araştırma; İstanbul kentinde özel araç kullanımının azaltılması ve yolculukların toplu taşıma yönlendirilmesi kapsamında park et devam et sisteminin etkinliğini ortaya koymaktadır. Bu kapsamda sürdürülebilir ve entegre ulaşımın gerekliliğinden bahsedilmiştir. Park et devam et sisteminin kente ve ulaşıma olan etkileri incelenmiştir. İnceleme alanı olan İstanbul kentinin park et devam et sisteminin konumları, kapasiteleri ve hangi ulaşım türü ile entegre olduğu araştırılmıştır. Bu bağlamda P+D sistemin erişilebilirliği, sürdürülebilir ulaşım dahilinde kente olan olumlu katkıları değerlendirilmiştir. İstanbul kentinde park et devam et sisteminin etkin olarak kullanıldığında trafik sıkışıklığını azaltmada önemli olduğu görülmüştür. Yapılan değerlendirme sonucunda kentlerde ulaşımın çevreye olan olumsuz etkilerinin azaltılması ve toplu taşımın desteklenmesi amacıyla P+D sisteminin önemli olduğunu söylemek mümkündür.
İNSANSIZ HAVA ARAÇLARININ (İHA) TAŞIMACILIKTA KULLANIMINA YÖNELİK KEŞFEDİCİ BİR ARAŞTIRMA: DRONE TAŞIMACILIĞI VE UYGULAMALARI
Authors: TURĞUT Muhammed , ŞEKER Bilal
Published: October 2022

Teknolojik gelişmelerle birlikte son dönemlerde insansız hava araçları (İHA) alanında önemli gelişmeler ve uygulamalar yaşanmaktadır. Özellikle askeri alanda çok fazla kullanılan İHA’ların ticari hayata adaptasyonu hızlı bir şekilde gerçekleşmektedir. Askeri gözetim ve izleme, telekomünikasyon, sağlık ve kurtarma operasyonları gibi çeşitli uygulamalarda büyük başarılar elde etmeye yardımcı olan İHA’lar dronelar aracılığıyla lojistik operasyonlarda da aktif rol almaya başlamışlardır. E-ticaretin günlük yaşama çok fazla entegre olması lojistik operasyonlarda yenilikçi çözümlere duyulan ihtiyacı da arttırmıştır. Bu kapsamda işletmeler müşteri taleplerine kesintisiz ve hızlı cevap verebilmek için drone taşımacılığını tercih etmeye başlamışlardır. Drone taşımacılığıyla birlikte erişimin zor olduğu, trafik sıkışıklığının yoğun olduğu ve henüz altyapı eksikliklerin tamamlanmadığı lokasyonlara da sevkiyat gerçekleştirilme imkânı doğmuştur. İlgili literatür incelendiğinde, son yıllarda uygulanan bu taşımacılık faaliyetinin incelenmesine yönelik çalışmaların kısıtlı olduğu görülmektedir. Bu çalışmada drone taşımacılığı ile ilgili kavramsal çerçeve anlatılarak, lojistik işletmelerindeki kullanımı uygulamalarıyla birlikte ele alınmıştır. Keşfedici bu araştırma aracılığıyla literatürdeki eksikliklerin giderilmesi hedeflenmiştir. Bu çalışma akademisyenler için drone taşımacılığının sektöre olan yansımalarını gösterirken, uygulamacılar için ise bir vizyon belirlemeyi amaçlamaktadır. Araştırma sonucunda drone taşımacılığının henüz yeni geliştirilen bir süreç olmasına rağmen lojistik işletmelerinde ve operasyonlarında kullanımının hızla arttığı ve yakın gelecekte de önemini arttıracağı tespit edilmiştir.
Reviews and Syntheses: Carbon biogeochemistry of Indian estuaries
Authors: Dutta Manab Kumar , Sreelash Krishnan , Padmalal Damodaran , Ward Nicholas D. , Bianchi Thomas S.
Published: October 2022

Abstract. The goal of this review is to provide a comprehensive overview of the magnitude and drivers of carbon cycling dynamics in the major estuaries of India. Data from a total of 32 estuaries along the Bay of Bengal (BB) and the Arabian Sea (AS) were compiled from the literature and re-analysed based on changes in season (wet vs. dry) and marine end-members (e.g., BB vs. AS). The estuaries are generally undersaturated in dissolved oxygen relative to the atmosphere and strongly influenced by local and regional precipitation patterns. Speciation of the dissolved inorganic carbon (DIC) pool is dominated by bicarbonate and primarily variability in DIC is controlled by a combination of carbonate weathering, the degree of precipitation, the length of the estuaries, in situ respiration, and mixing. Carbonate dissolution had the largest influence on DIC during the wet season, while respiration was the primary control of DIC variability in the estuaries connected with BB during the dry season. Interestingly, the influence of anaerobic metabolism on DIC is observed in the oxygenated mangrove dominated estuaries, which we hypothesize is driven by porewater exchange in intertidal sediments. Dissolved organic carbon (DOC) generally behaves non-conservatively in the studied estuaries. The DOC-particulate organic carbon (POC) inter-conversion and DOC mineralization are evident in the BB during the dry season and AS estuaries, respectively. The wet season δ13CPOC shows dominance of freshwater algae, C3 plant material, as well as marine organic matter in POC. However, anthropogenic inputs are evident in some estuaries in eastern India during the dry season. POC respiration was identified in the AS; however, a link between POC and CH4 is identified throughout both the regions. pCO2 is controlled principally by respiration with freshwater discharge only playing a marginal important role in the BB. The AS estuaries act as a CO2 source to the atmosphere; however, the BB estuaries vary between a source and sink. POC together with methanotrophy and dam abundance appear to control CH4 concentrations, and all of the studied estuaries act as a CH4 source to the atmosphere. Additionally, anthropogenic inputs and groundwater exchange also show potential influences in some cases. The Indian estuaries contribute 2.62 % and 1.09 % to the global riverine DIC and DOC exports to the ocean, respectively. The total CO2 and CH4 fluxes from Indian estuaries are estimated as ~9718 Gg yr-1 and 3.27 Gg yr-1, which contributes ~0.67 % and ~0.12 %, respectively, to global estimates of estuarine greenhouse gas emissions. While a qualitative idea on the major factors controlling the carbon biogeochemistry in India is presented through this work, a more thorough investigation including rate quantification of the above-mentioned mechanisms is essential for precise accounting of the C budget of Indian estuaries.
SIMULATING LIDAR TO CREATE TRAINING DATA FOR MACHINE LEARNING ON 3D POINT CLOUDS
Authors: Hildebrand J. , Schulz S. , Richter R. , Döllner J.
Published: October 2022

Abstract. 3D point clouds represent an essential category of geodata used in a variety of geoinformation applications. Typically, these applications require additional semantics to operate on subsets of the data like selected objects or surface categories. Machine learning approaches are increasingly used for classification. They operate directly on 3D point clouds and require large amounts of training data. An adequate amount of high-quality training data is often not available or has to be created manually. In this paper, we introduce a system for virtual laser scanning to create 3D point clouds with semantics information by utilizing 3D models. In particular, our system creates 3D point clouds with the same characteristics regarding density, occlusion, and scan pattern as those 3D point clouds captured in the real world. We evaluate our system with different data sets and show the potential to use the data to train neural networks for 3D point cloud classification.
GEOREFERENCING IN IFC: MEETING THE REQUIREMENTS OF INFRASTRUCTURE AND BUILDING INDUSTRIES
Authors: Jaud Š. , Clemen C. , Muhič S. , Borrmann A.
Published: October 2022

Abstract. The problem of georeferencing building information modelling (BIM) models is complex and in need of a comprehensive solution. We focus on the open BIM data format Industry Foundation Classes (IFC) and its georeferencing implementation. The requirements voiced by the domain experts during recent years have been collected and analysed. While IFC already covers some of the concepts, we propose an extension to the IFC schema to handle the inadequacy. Our proposal composes of two new entities: one supports geographic coordinate reference systems (CRSs) and the other enables a rigid transformation of BIM geometries. We showcase the possibilities with three examples, one for each of the required scenarios. The improvements assure much-needed semantically clear definitions of the georeferencing concept within the IFC data model. As such, the interpretation of IFC data content is unambiguous for stakeholders and software implementers.
PCSERVE – ND-POINTCLOUDS RETRIEVAL OVER THE WEB
Authors: Meijers M.
Published: October 2022

Abstract. We investigate how PCServe, a web service for disseminating massive point clouds, performs for read-only access (i.e. a visualization application). PCServe is backed by a database model based on Space Filling Curves. By adding a virtual hierarchy of blocks to the database, we can support different visualization applications for retrieval of point cloud data over the web without having to store the data multiple times. This makes expressive access to point clouds over the web possible. We investigate the amount of processing that is needed to create the database model and how well PCServe handles requests from the visualization application. Some suggestions are provided how the current approach can be improved.
PATH-TRACING SEMANTIC NETWORKS TO INTERPRET CHANGES IN SEMANTIC 3D CITY MODELS
Authors: Nguyen S. H. , Kolbe T. H.
Published: October 2022

Abstract. Recent years have seen a significant and rapid rise in the development and deployment of many urban digital twins worldwide. Urban digital twins provide a central platform for incorporating data and knowledge from different sources and fields, and can thus be used for many urban-related processes such as urban planning, analysis, monitoring and visualization. One of the key requirements of digital twins in general and urban digital twins in particular is the continuous, bidirectional data flow between the physical entity and its digital counterpart. Consequently, this requires the ability to both detect and understand changes between different temporal versions of the physical and digital entities. In the context of smart cities and semantic 3D city models however, only a few studies have addressed this so far. This is due to the facts that: (1) Semantic 3D city models (mostly encoded in CityGML) contain multifaceted information modelled in a complex inheritance hierarchy, which complicates their change detection process; (2) As cities constantly evolve over time, matching their datasets often results in a large number of changes, which must be further analysed and processed to produce meaningful information; (3) Individual changes in the datasets are multi-layered as well as often correlated and cannot be fully understood without considering their context in the city model; and (4) Different types of changes are perceived and interpreted differently by different stakeholders, meaning that the outcome of the change detection and interpretation process must ultimately serve the human factor. Therefore, to address these challenges, this research proposes a Path-tracing Semantic Network (PSN) to interpret detected changes in semantic 3D city models. The framework represents the multidimensional nature of changes together with stakeholders in a semantic network, where their interrelations can be analysed explicitly using graph-based path-tracing methods.
DATASET FOR URBAN SCALE BUILDING STOCK MODELLING: IDENTIFICATION AND REVIEW OF POTENTIAL DATA COLLECTION APPROACHES
Authors: Pei W. Y. , Biljecki F. , Stouffs R.
Published: October 2022

Abstract. Construction materials play an important role in environmental impacts and make cities big resource consumers. To assess the sustainability of cities, the combined use of Life Cycle Assessment (LCA) and Material Flow Analysis (MFA) is considered effective to analyze construction material stock and flows. However, exhaustive data is required for such analyses, making LCA and MFA difficult to apply at the urban scale. Building information, the essential ingredient, is rarely available openly. Common approaches to gather the required data include both obtaining it directly from available datasets, e.g. open data from official sources, and indirectly generating data based on available data, e.g. using machine learning to fill the missing gaps. This research develops a data collection guideline for buildings’ geometrical features, components and materials at the urban scale in the context of LCA and MFA. First, it identifies the basic steps of urban-scale building stock modelling and the list of data requirements. Second, the factors influencing the data collection are pointed out. In line with these guidelines, this research picks Singapore as a study area, reviewing the relevant authoritative open data sources and methodologies to estimate missing data. Finally, the suggestion on implementation of data collection are provided. When the data collection for urban scale stock modelling is limited by uncertain reality conditions, identifying and combining open datasets and data generation methods for data preparation is a necessity.
AUTOMATIC BUILDING FOOTPRINT EXTRACTION FROM 3D LASERSCANS
Authors: Rottmann P. , Haunert J.-H. , Dehbi Y.
Published: October 2022

Abstract. Building footprints are a prerequisite for many tasks such as urban mapping and planning. Such structures are mostly derived using airborne laser scanning which reveals rather roof structures than the underlying hidden footprint boundary. This paper introduces an approach to extract a 2D building boundary from a 3D point cloud stemming from either terrestrial scanning or via close-range sensing using a mobile platform, e.g. drone. To this end, a pipeline of methods including non-parametric kernel density estimation (KDE) of an underlying probability density function, a solution of the Travelling Salesperson Problem (TSP), outlier elimination and line segmentation are presented to extract the underlying building footprint. KDE turns out to be suitable to automatically determine a horizontal cut in the point cloud. An ordering of the resulting points in this cut using a shortest possible tour based on TSP allows for the application of existing line segmentation algorithms, otherwise dedicated to indoor segmentation. Outliers in the resulting segments are removed using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The segments are then generalized leading to the final footprint geometry. We applied our approach on real-world examples and achieved an IoU between 0.930 and 0.998 assessed by ground truth footprints from both authoritative and volunteered geographic information (VGI) data.
THE SPATIAL DATA INFRASTRUCTURE OF AN URBAN DIGITAL TWIN IN THE BUILDING ENERGY DOMAIN USING OGC STANDARDS
Authors: Santhanavanich T. , Padsala R. , Würstle P. , Coors V.
Published: October 2022

Abstract. As the world has more urbanized, cities need to assess and manage their building energy performances in order to achieve energy-reduction goals. The urban digital twins (UDT) offer promising solutions to this demand by providing valuable insights with qualitative and quantitative information about the building environment. The urban building energy data is not only measured from the equipped sensor devices but can also be simulated based on the analysis software. In this research, we aim to explore the development of the spatial data infrastructure (SDI) for managing building energy in the UDT application by employing the Open Geospatial Consortium (OGC) standards which increases the data usability and efficiency. In our concept SDI, the big data in the UDT application is managed with the OGC specifications as follows: OGC SensorThings API (STA) for data with Spatio-temporal characteristics, OGC API 3D GeoVolumes for 3D geospatial content delivery, OGC CityGML for virtual 3D city models, OGC API Features, Web Feature Service (WFS), and Web Map Service (WMS) for other 2D geospatial contents. This concept enables broad interoperability between multiple data layers and client applications. As a proof of concept, we developed the UDT application called with a highly visual and intuitive user interface using the proposed SDI concept as a part of the EnSysLE project in the study area of three regions in Germany: Ludwigsburg, Dithmarschen, and Ilm-Kreis. The proposed concept can be expanded to other UDT domains and on a larger scale in future work.
VIABILITY TESTING OF GAME ENGINE USAGE FOR VISUALIZATION OF 3D GEOSPATIAL DATA WITH OGC STANDARDS
Authors: Würstle P. , Padsala R. , Santhanavanich T. , Coors V.
Published: October 2022

Abstract. Urban digital twins have become an essential factor for cities and communities to visualize, simulate and analyze data. The conventional geospatial standards work great with online platforms such as CesiumJS or ArcGIS API for JavaScript. However, their usage in different platforms such as game engines has not been well established yet. Game engines provide an interesting application case because they offer a different approach to visualizing large city models and provide a high graphical fidelity.This paper aims to answer how the existing standards, such as the API standards GeoVolumes and SensorThings, as well as the 3D model standards 3D Tiles and Esri Indexed 3D Scene Layer (I3S), can interact with game engines. For this purpose, three use-cases were selected and have been used to build applications. These focus on using sensor data in AR and different city development scenarios in a digital environment.This study shows that different geospatial standard formats such as 3D Tiles, I3S, and GL Transmission Format (glTF) can be used in game engines, either directly or over a GeoVolumes Server. Their implementation makes it possible to use the advantages of game engines with real-world datasets.
DYNAMIC AND WEB-BASED 4D VISUALIZATION OF STREETSPACE ACTIVITIES DERIVED FROM TRAFFIC SIMULATIONS AND SEMANTIC 3D CITY MODELS
Authors: Beil C. , Kendir M. , Ruhdorfer R. , Kolbe T. H.
Published: October 2022

Abstract. Semantic 3D city models can serve as anchor points for different components of urban digital twins. In addition to static 3D models such as buildings, transportation infrastructure, vegetation, or city furniture, this can also include dynamic processes such as traffic movement or changing traffic signals. Integrating these aspects into a dynamic, realistic, and accessible 4D visualization presents a number of requirements and challenges, which are discussed. While the City Geography Markup Language (CityGML) is a well established OGC standard for modeling and exchanging semantic 3D city models, the Cesium Language (CZML) provides capabilities for visualizing time-dependant properties that can be displayed in the Cesium virtual globe. Results of the open-source microscopic traffic simulation tool SUMO include information on locations and orientations of vehicles, bicycles, pedestrians and other traffic members as well as traffic signal information and can be exported as a CSV table. In order to allow a 4D visualization of these simulation results, a scheme for deriving 3D + t CZML documents from data given as 2D + t CSV files is presented. Additionally, dynamically changing traffic lights are integrated. Based on data available for the city of Munich, a CityGMLcompliant streetspace model is generated. This enables the combination of traffic simulation results and semantic 3D city models within a common Cesium based web-visualization and allows a direct and platform independent access to realistic visualizations of streetspace activities. Since this often results in a huge amount of data, a spatio-temporal tiling strategy enabling the visualization of large CZML data is presented.
DEFINING THE FUNCTIONAL SPACE OF BUS STOPS FROM MLS POINT CLOUDS
Authors: González-Collazo S. M. , Balado J. , González E. , Lorenzo H. , Díaz-Vilariño L.
Published: October 2022

Abstract. Urban transports are essential for the mobility of citizens, therefore, functional spaces allocated to them should be correctly defined. This research defines the functional spaces for bus stops based on Mobile Laser Scanning (MLS) point clouds. Specifically, three functional spaces are defined; functional space allocated for the marquee (pedestrian space), functional space allocated for the road markings (bus space), and functional space allocated for the bus stop (pedestrians and bus). The method presented is divided into four stages; 1) Bus stop location in the point cloud from given geographical coordinates, 2) Selection of the region of interest (ROI), 3) Classification of the elements in the ROI, 4) Functional space definition. Several algorithms are used to reduce the computational time (point density reduction) and to classify the different elements contained in the bus stop (DBSCAN, height, width, and intensity filter). In addition, bounding boxes are defined to delimit the functional spaces, being compared with the ground truth box. Seven bus stops sited in Palencia (Spain) were analysed. Results show an F1-score value of 0.98 regarding the classification of the elements. Functional spaces allocated for marquees were perfectly defined. Although most of the estimated functional spaces allocated for the bus and the bus stop have high accuracy, in some cases this accuracy decreases due to the functional space allocated for the bus was overestimated.
MOBILITY RESILIENCE OF COMMUTE TRIPS DURING THE COVID-19 PANDEMIC IN SEOUL, KOREA
Authors: Li X. , Ha J. , Lee S.
Published: October 2022

Abstract. Since early 2020, the number of COVID-19 cases has continued its rise and fall worldwide, greatly impacting sectors such as health outcomes, economics, housing, and transportation. To mitigate the spread of the pandemic, governments implemented various measures to reduce the mobility of the population, restricting international travel, hierarchical lockdowns, stay-at-home mandates, and work-from-home orders. In this aspect, early studies in the transportation field showed large changes in travel behaviour. However, we know less about the long-term impact of COVID-19 on people's travel behaviour. This paper explores the change in commute behaviour during the pandemic, focusing on the resilience index of transit users and its determining factors. The hist gradient boosting model was the most precise when compared with linear and other machine learning models (considering R2, MSE, MAE). The results suggested the following: (1) commuters' trips decreased unevenly in Seoul. Through machine learning algorithms, social-economic factors, and accessibility, 50% of the heterogeneity can be explained. (2) Consumer and Service Industry and Foreigner Tourism were impacted negatively continually. Neighbourhoods with higher car ownership and a higher percentage of female residents show long term weak public transit resilience. (2) Short distance commuters (less than 20 minutes) and commuters visiting city centres, returned to public transport in the second year after avoiding it during the first year of the pandemic. Considering the uneven negative results of COVID-19, this research can be a reference for policy design and effective decision making.
4SA: OPTIMIZING SPACE FILLING CURVE BASED GRID CELL INDEXING TO SCALABLY MANAGE REMOTELY SENSED IMAGES IN KEY-VALUE DATABASES
Authors: Lokugam Hewage C. N. , Vo A. V. , Bertolotto M. , Le-Khac N.-A. , Laefer D.
Published: October 2022

Abstract. State-of-the-art remote sensing image management systems adopt scalable databases and employ sophisticated indexing techniques to perform window and containment queries. Many rely on space-filling curve (SFC) based index techniques designed for key-value databases and are predominantly employable for images that are iso-oriented. Critically, these indexes do not consider the high degree of overlap among images that exists in many data sets and the affiliated storage requirements. Specifically, employing an SFC-based grid cell index approach in consort with ground footprint coverage of the images requires storage of a unique image object identification (IOI) for each image in every grid cell where overlap occurs. Such an approach adversely affects both storage and query response times. In response, this paper presents an optimization technique for an SFC-based grid cell space indexing. The optimization is specifically designed for window and containment queries where the region of interest overlaps with at least a 2 × 2 grid of cells. The technique is based on four cell removal steps, thus called “four step algorithm” (4SA). Each step employs a unique spatial configuration to check for continuous spatial extent. If present, the IOI of the target cell is omitted from further consideration. Analysis and experiments on real world and synthetic image data demonstrated that 4SA improved storage demands by 41.3% – 47.8%. Furthermore, in the performed querying experiments, only 42% of IOI elements needed to be processed, thus yielding a 58% productivity gain. The reduction of IOI elements in querying also impacted the CPU execution time (3.0% – 5.2%). The 4SA also demonstrated data scalability and concurrent user scalability in querying large regions by completing the index searching and concurrent user scalability 1.86% – 3.35% faster than when 4SA was not applied.
SMART TRANSPORT FOR SMART CITIES: A FUTURISTIC SCENARIO AND A REALISTIC PROJECT
Authors: Pantazis D. N. , Argyrakis P. , Moussas V. , Geroulis G. , Daverona A. C. , Stergiou C. , Voyiatzis I. , Aposporis P.
Published: October 2022

Abstract. Smart transport for smart cities: our article presents an innovative and original idea in the field of urban air transport with a new type of manned drone. This scenario is analysed starting from some initial futuristic ideas up to the reality of the construction of such a vehicle. After a brief presentation of a list of manned aerial vehicles (manned drones) that reflects the efforts that have been realized until now, a number of parameters and specifications related to such aerial urban transport, are also pointed out. In addition, various emerging issues comprising legislation matters, infrastructure for landing and take-off in cities, navigation methods and other possible impacts on the urban transport, have also been presented shortly.
TOWARDS MORE RESILIENT SMART CITIES: MT-InSAR MONITORING OF URBAN INFRASTRUCTURE USING MACHINE LEARNING TECHNIQUES
Authors: Rodríguez-Antuñano I. , Martínez-Sánchez J. , Lagüela-López S. , Riveiro B.
Published: October 2022

Abstract. Global climate change makes the maintenance and resilience of cities one of the greatest challenges facing civilization. Constant monitoring is essential work to determine if the city and its infrastructure are well preserved. This monitoring has become increasingly simple and affordable, thanks to the advancement of technology, so the current trend is to systematically monitor the entire city (this concept is the paradigm of the smart city). There are several methods for infrastructure monitoring including GPS, mobile-mapping, video-surveillance, etc. However, this type of method has a series of disadvantages, such as the impossibility of obtaining large-scale data or the unavailability of information of the previous, current or after state that an event has occurred in the study-area. This can be solved with monitoring based on satellite images, since these have historical and constant coverage over time, with good resolution to identify urban structures and cover large study areas. The use of satellite radar images through MT-InSAR is booming because it is a powerful remote sensing technique capable of detecting displacements on the earth's surface. This technique can be combined with Machine-Learning to perform predictive analysis in urban environments and detect infrastructure failures. This predictive monitoring capable of anticipating risks is one of the objectives of the new urbanism. For this reason, this work analyzes the collapse of a pier, which occurred in Vigo, a city in NW Spain, through radar satellite images (Sentinel-1), MT-InSAR and Machine-Learning. The result is the possibility of anticipating structural failures thanks to the predictive monitoring.
INSIGHTS TO 11 SMART CITIES OF UTTAR PRADESH, INDIA THROUGH SPATIAL PATTERN ANALYSIS OF LAND USE/ LAND COVER
Authors: Verma R. , Zawadzka J. , Garg P. K.
Published: October 2022

Abstract. Urban planning of a smart city needs to be done in conjunction to Urban Green Spaces (UGS). Landscape Metrics are one of the efficient ways to analyze the patterns of Land use/Land cover (LU/LC) in a study area. Spatio-temporal change in the urban dynamics of the 11 smart cities of Uttar Pradesh state of India namely "Agra", "Aligarh", "Bareilly", "Jhansi", "Kanpur", "Lucknow", "Moradabad", "Prayagraj", "Rampur", "Saharanpur" and "Varanasi" are studied using Landscape Metrics with the help of publically available classified data such as, for years 1985, 1995 and 2005, Decadal Land use data of India and for year 2015, Copernicus Global Land service Dynamic Land Cover layers (CGLS-LC100 products). Landscape Shape Index (LSI), Largest Patch Index (LPI), Mean Euclidean Nearest Neighbor Distance (ENN_MN) and Aggregation Index (AI) are 4 general metrics used to map LU/LC patterns. Results indicate high positive relationship between LSI and AI values but negligible relationship between LPI and ENN_MN values of built-up and vegetation patches in study area. LPI and LSI show increase in values over the years with LSI showing more steep change in duration, but values of ENN_MN and AI show gradual decreasing trend over the years. Among 11 smart cities, only “Kanpur”, “Agra” and “Moradabad” are most similar in values at higher, average and lower side of metrics with “Lucknow” showing highest complexity for both classes. In general, a heterogenous growth of patches appear in the study area with “Rampur” being the most consistent in metrics while “Jhansi” and “Saharanpur” being most inconsistent.
NOVEL APPROACH FOR FOREST ALLOMETRIC EQUATION MODELLING WITH RANSAC SHAPE DETECTION USING TERRESTRIAL LASER SCANNER
Authors: Singh A. , Kushwaha S. K. P. , Nandy S. , Padalia H.
Published: October 2022

Abstract. Forest biomass quantification is highly crucial for the maintenance of global carbon cycle. Hence, the accurate estimation of tree parameters is of utmost requirement nowadays. The hypothesis of this research is to formulate a volumetric equation which will be focussed on the structure of the tree and not the species. To serve this purpose, 13 plots were scanned in the Barkot forest range of Uttarakhand, India, and the plots were scanned using Terrestrial Laser Scanner (TLS). The tree attribute such as radius of the stem and tree height were estimated using Random Sample Consensus (RANSAC) algorithm and line primitive vector, respectively. The correlation was established using multiple linear regression using the TLS derived tree parameters and field estimated tree parameters. The R2 obtained for field estimated biomass is 0.91 with radius and 0.73 with the tree height. The radius of the stem and tree height were used for the calculation of volume of all the trees and the R2 value obtained for field estimated volume and TLS derived volume is 0.95. Then the stem volume of the trees were calculated based on the new volume equation. The statistical analysis was done, and ANOVA test was performed indicating high F value (25.4) which shows that the regression model is significant. The predicted biomass correlation coefficient (R2 = 0.97) was obtained. It represents the significance of the model for the estimation of volume and biomass is high. The equation can be implemented in any forest type irrespective of the forest structure.
3D BASED SMOKE FIRE EMISSION AND EMERGENCY EVACUATION SIMULATION IN HIGH-RISE BUILDINGS
Authors: Taleai M. , Mokhtari M. , Zlatanova S.
Published: October 2022

Abstract. High-rise buildings, with high number of population, have always been among the concerns discussed in urban crisis management. One of the crucial aspects of high-rise buildings is having a proper and timely action plan for evacuation in the case of emergencies such as fires. The spread and progression of fire smoke in high-rise buildings are affected by the architectural design of the building, and having a three-dimensional (3D) model of high-rise buildings can play key role in damage reduction in such cases. This paper, first presents a method for 3D smoke emission based on utilizing 2D cadastral data to generate a 3D model of the buildings. Then, in step of smoke fire emission simulation, the influence of different smoke movement paths such as the entrance/exit doors, windows, smoke barrier, stairs, and elevators, are examined in different scenarios. Next, the emergency evacuation of occupancies with different behavioral and physical characteristics is simulated. The results indicate that 2D cadastral data of apartments can be employed as a suitable source to create 3D models of high-rise buildings, and allows to examine the emergency evacuation of residents by including the smoke simulation spread in the building.
USE CASES FOR DISTRICT-SCALE URBAN DIGITAL TWINS
Authors: Alva P. , Biljecki F. , Stouffs R.
Published: October 2022

Abstract. Efficient usage and management of abundant data are crucial for organisations, especially in the light of a lack of in-depth IT knowledge. Digital twins (DTs) are particularly expected to assist organisational processes, as behaviours of physical components are realistically represented by them using data for individual use cases. Nevertheless, DTs are extremely reliant on their use cases, leading to an extensive DT catalogue. However, a conclusive list of use cases for this accumulation of feasible DT application areas does not exist. To address this issue, this paper documents the use cases of Urban Digital Twin (UDT) platforms and applications with a state-of-the-art review. The study focuses on district-scale UDT applications that model, manage and analyse—buildings, transportation, energy, water, utility, and infrastructures that form smart cities. In order to catalogue diverse use cases found in several sectors, theoretical reasoning is developed. Our study provides a classified inventory that can be helpful for stakeholders in companies, government agencies and academia—such as researchers, architects, facilities managers, developers, and city planners.
ENRICHMENT OF UAV PHOTOGRAMMETRIC POINT CLOUD TO ENHANCE DSM IN A DENSE URBAN REGION
Authors: Kushwaha S. K. P. , Mokros M. , Jain K.
Published: October 2022

Abstract. Unmanned Arial Vehicle (UAV) have become very cost effective and time efficient technology for surveying, mapping and various other geospatial applications. UAV serves as a platform but the main dependency also lies on which type of the sensor is integrated on it. Optical photogrammetric surveys are more often conducted for generalized purposes and for its advantages. UAV photogrammetry have become very useful tool in creation of 3D city model or Digital twin model. But creating a Digital Surface Model (DSM) through photogrammetry is not that efficient when it comes to very dense urban regions. In this research, the quality of DSM, Noise in the Photogrammetric point cloud are studied, then an experimental approach is tried to enhance the DSM than that of DSM obtained from photogrammetric processing software. From the results, it is observed that the quality of DSM is enhanced in narrow streets/roads, near trees, regions between trees and buildings, regions near building and road, etc. This approach can be utilized for low-cost UAV photogrammetry for accurate 3D modelling of the narrow streets or roads are of critical importance.
SPATIOTEMPORAL ANALYSIS OF URBAN GROWTH AND LAND SURFACE TEMPERATURE: A CASE STUDY OF LAHORE, PAKISTAN
Authors: Basheer M. A. , Waseem M. A.
Published: October 2022

Abstract. Cities around the world are facing tremendous pressure due to rapid urbanization. They are being extended haphazardly especially in developing countries, putting strain on already depleting natural recourses. The land-use conversion to built-up areas harms the urban environment significantly. The most immediate implications of this land-use/land cover (LULC) conversion are the transition of Land Surface Temperature (LST) and the creation of Urban Heat Islands. This research investigates the spatial distribution of LST and LULC and their interrelation using satellite images from Landsat 5 (TM) and 8 (OLI/TRS) for the years 1998, 2010, and 2021. The built-up area in Lahore has grown enormously over the last two decades. Our results indicate that each year 1.26% of the land is being transformed into built-up area. Consequently, the prevailing urban development trends have also influenced the LST. In particular, we observed an average upsurge of 0.47°C per year between 1998 and 2021. If our cities continue to expanqd in the same manner, this would have serious ecological implications in the future. Thus, urban planners and policymakers need to incorporate climate-adaptive design at the community and building levels to improve the situation.
LAST MILE LOGISTICS: IMPACT OF UNSTRUCTURED ADDRESSES ON DELIVERY TIMES
Authors: Abdul Rahman M. , Aamir Basheer M. , Khalid Z. , Tahir M. , Uppal M.
Published: October 2022

Abstract. The e-commerce industry has seen significant growth over the past few years. One significant issue that has sprung up as a result of this growth is unstructured addresses during last mile delivery. These ambiguous addresses are an established issue, particularly in developing countries like Pakistan. They are difficult to read and locate by last mile delivery riders thereby increasing delivery times and cost, negatively impacting the business of the company. Increased delivery times are also detrimental to the environment. In this paper, we aim to quantify the effects of unstructured addresses on last mile logistics. Many attempts have been made to standardise addresses to tackle this problem. Deep learning based approaches using recurrent neural networks (RNN) as well as probabilistic approaches using hidden Markov models (HMM) have been used. However, the main downside to these approaches are the underlying variation in address schemes in housing societies. We present an end to end rule based pipeline using Levenshtein distance (LD) and regular expressions (RegEx) rules which takes those unstructured addresses and outputs their structured forms along with their Geo-coordinates. The pipeline also returns the optimized route to minimize the last mile distance traveled.
PEDESTRIAN ACCESSIBILITY ASSESSMENT USING SPATIAL AND NETWORK ANALYSIS: A CASE OF SOFIA CITY
Authors: Karamitov K. , Petrova-Antonova D.
Published: October 2022

Abstract. Walking is the most accessible form of mobility, delivering different benefits to the cities and their residents such as improved public health, community liveability, efficient land use and cost savings in terms of needed space to allow movement. Walkable neighbourhoods provide a high-quality urban environment with public spaces and a variety of activities and services. To increase walkability, a focus should be placed on the pedestrian network which integrates all transportation modes, making it a subject of network-based analyses related to pedestrian accessibility, connectivity, and walkability. This paper aims to assess pedestrian accessibility in Sofia, Bulgaria by applying network and spatial analysis. It analyses the macro scale of the integration and availability of the street network. It also investigates the access to selected urban facilities, which help to increase the quality of life, such as public transport stops and parks. The results prove that such analyses could support the assessment of the feasibility of infrastructure projects and their efficiency. The analyses show that Sofia has a very good distribution of public transport stops and the parks and smaller gardens are also relatively evenly spread across the urban area. Despite this, the results discover some areas where specific measures need to be taken to provide pedestrian access to these amenities, especially in neighbourhoods where investments are high, and the need for such access is increasing.