bluuebunny/crossref_metadata_2025 huggingface dataset:
A NOVEL DEEP LEARNING BASED METHOD FOR DETECTION AND COUNTING OF VEHICLES IN URBAN TRAFFIC SURVEILLANCE SYSTEMS
Authors: Majin J. J. , Valencia Y. M. , Stivanello M. E. , Stemmer M. R. , Salazar J. D.
Published: June 2021

Abstract. In intelligent transportation systems (ITS), it is essential to obtain reliable statistics of the vehicular flow in order to create urban traffic management strategies. These systems have benefited from the increase in computational resources and the improvement of image processing methods, especially in object detection based on deep learning. This paper proposes a method for vehicle counting composed of three stages: object detection, tracking and trajectory processing. In order to select the detection model with the best trade-off between accuracy and speed, the following one-stage detection models were compared: SSD512, CenterNet, Efficiedet-D0 and YOLO family models (v2, v3 and v4). Experimental results conducted on the benchmark dataset show that the best rates among the detection models were obtained using YOLOv4 with mAP = 87% and a processing speed of 18 FPS. On the other hand, the accuracy obtained in the proposed counting method was 94% with a real-time processing rate lower than 1.9.
INTEGRATING TOPOGRAPHIC, PHOTOGRAMMETRIC AND LASER SCANNING TECHNIQUES FOR A SCAN-TO-BIM PROCESS
Authors: Lo Brutto M. , Iuculano E. , Lo Giudice P.
Published: June 2021

Abstract. The preservation of historic buildings can often be particularly difficult due to the lack of detailed information about architectural features, construction details, etc.. However, in recent years considerable technological innovation in the field of Architecture, Engineering, and Construction (AEC) has been achieved by the Building Information Modeling (BIM) process. BIM was developed as a methodology used mainly for new construction but, given its considerable potential, this approach can also be successfully used for existing buildings, especially for buildings of historical and architectural value. In this case, it is more properly referred to as Historic – or Heritage – Building Information Modeling (HBIM). In the HBIM process, it is essential to precede the parametric modeling phase of the building with a detailed 3D survey that allows the acquisition of all geometric information. This methodology, called Scan-to-BIM, involves the use of 3D survey techniques for the production of point clouds as a geometric “database” for parametric modeling. The Scan-to-BIM approach can have several issues relating to the complexity of the survey. The work aims to apply the Scan-to-BIM approach to the survey and modeling of a historical and architectural valuable building to test a survey method, based on integrating different techniques (topography, photogrammetry and laser scanning), that improves the data acquisition phase. The “Real Cantina Borbonica” (Cellar of Royal House of Bourbon) in Partinico (Sicily, Italy) was chosen as a case study. The work has allowed achieving the HBIM of the “Real Cantina Borbonica” and testing an approach based exclusively on a topographic constraint to merge in the same reference system all the survey data (laser scanner and photogrammetric point clouds).
TOWARDS LIFELONG CROP RECOGNITION USING FULLY CONVOLUTIONAL RECURRENT NETWORKS AND SAR IMAGE SEQUENCES
Authors: Chamorro J. A. , Feitosa R. Q. , Happ P. N. , Bermudez J. D.
Published: June 2021

Abstract. Recent works have studied crop recognition in regions with highly complex spatio-temporal dynamics typical of a tropical climate. However, most proposals have only been evaluated in a single agricultural year, and their capabilities to generalize to dates outside the temporal sequence have not been properly addressed thus far. This work assesses the generalization capabilities of a recent convolutional recurrent architecture, testing it in a temporal sequence two years ahead of the sequence with which it was trained. Furthermore, a N-to-1 variant of such network is proposed, which is able to produce classification outcomes for every month in the agricultural year, and it is compared with two baselines designed in a more traditional approach, in which a separate specific network is trained for each month of the year. The approaches are evaluated on two public datasets from a tropical region. The first dataset comprehends the period from June 2017 to May 2018, while the second goes from October 2019 to September 2020. Results show a decrease of up to 24.6% in per-date average F1 score when training the network with data of an agricultural year different from the one it is tested on, which indicates a domain shift that demands further research. Additionally, the proposed approach presented only a slight decrease in performance compared to its baseline when trained on the same dataset, with a 2.7% drop in average F1 score. This performance drop is a small cost in exchange for its operational advantages, such as reduced training time and a more straightforward pipeline.
PREFACE: THE 2021 EDITION OF THE XXIVTH ISPRS CONGRESS
Authors: Mallet C. , Lafarge F. , Poreba M. , Wu T. , Bahl G. , Yu M. , Garioud M. , Chen Y. , Jiang S. , Yang M. Y. , Paparoditis N.
Published: June 2021

Abstract. We report key elements and figures related to the proceedings of the 2021 edition of the XXIVth ISPRS Congress. Similarly to 2020, the COVID-19 pandemic caused global travel challenges and restrictions for the first half of 2021. Consequently, the physical Congress re-scheduled from June 2020 to July 2021 was again postponed to June 2022, still in Nice (France). Papers were already submitted and the ISPRS Council decided to carry out the review process and the publication of the proceedings of the papers submitted under the label “2021 Edition”. The authors of published papers had the opportunity to present their work during a Digital Event, this year scheduled the same week as the planned Congress (5–9 July 2021).
USING TAILORED GRAPHICAL TOOLS TO IMPROVE THE QUALITY OF DISPLACEMENT MAPS AT CTTC: THE VETOOLS
Authors: Navarro J. A. , Barra A. , Monserrat O. , Crosetto M.
Published: June 2021

Abstract. The Geomatics Division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) has been producing terrain displacement maps for more than 15 years using the PSIG software chain, developed entirely by the members of the aforesaid group. PSIG has reached a high level of maturity, being highly automated but also offering the user the possibility to fine-tune the set of critical parameters controlling the process. However, large areas with a high level of diversity often pose problems in finding a good quality solution using a single set of parameters. Improving the quality of the final terrain displacement map means being able to process very local and critical areas using specific sets of parameters; however, identifying such areas is a difficult process without suitable analysis tools. The VETools, a new software project still under development, but very close to its completion, target this problem. With the VETools it is possible to visualize the results produced by a previous PSIG global processing, analysing their quality, thus making possible to identify the local, critical areas, allowing the user to interactively experiment with specific sets of parameters for these areas, reprocessing them and reviewing the local results as many times as desired and, finally, merging all of them in a single, unique solution whose level of quality is appropriate for the whole area of interest. This work presents the current state of development of the VETools, describing their features and the challenges overcome.
ACTIVE REFLECTORS FOR INTERFEROMETRIC SAR DEFORMATION MEASUREMENT
Authors: Wassie Y. , Crosetto M. , Luzi G. , Monserrat O. , Barra A. , Palamá R. , Cuevas-González M. , Mirmazloumi S. M. , Espín-López P. , Crippa B.
Published: June 2021

Abstract. This paper is focused on the design, implementation and testing of an active reflector, to be used to support deformation monitoring studies based on Synthetic Aperture Radar interferometry. The device is designed to work in C-band with Sentinel-1 data, operating at 5.405 GHz ± 50 MHz. A brief description of the active reflector is provided. It consists of two antennas and an amplifying section. The active reflector has been tested in different experiments. In this paper, we describe the experiment carried out in the Parc Mediterrani de la Tecnologia (Castelldefels, Barcelona). The result shows a strong correlation with temperature. A calibration test was carried out to experimentally derive a calibration curve to correct the effect of temperature on phase stability.
ANALYSIS AND DETECTION OF WILDFIRES BY USING PRISMA HYPERSPECTRAL IMAGERY
Authors: Spiller D. , Ansalone L. , Amici S. , Piscini A. , Mathieu P. P.
Published: June 2021

Abstract. This paper deals with the analysis and detection of wildfires by using PRISMA imagery. Precursore IperSpettrale della Mis­sione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 0.4–2.5 µm and an average spectral resolution less than 10 nm. In this work, we used the PRISMA hypercube acquired during the Australian bushfires of December 2019 in New South Wales. The analysis of the image is presented considering the unique amount of information contained in the continuous spectral signature of the hypercube. The Carbon dioxide Continuum-Interpolated Band Ratio (CO2 CIBR), Hyperspectral Fire Detection Index (HFDI), and Normalized Burn Index (NBR) will be used to analyze the informative content of the image, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. A multiclass classification is presented by using a I-dimensional convolutional neural network (CNN), and the results will be com­pared with the ones given by a support vector machine classifier reported in literature. Finally, some preliminary results related to wildfire temperature estimation are presented.
ASSESSMENT OF FLOODED AREAS CAUSED BY A DAM BREAK (SARDOBA DAM, UZBEKISTAN)
Authors: Tavus B. , Kocaman S. , Gokceoglu C.
Published: June 2021

Abstract. Although dams are very useful engineering structures, they can have extremely harmful consequences if they fail. One example of these failures occurred in Sardoba Reservoir (Uzbekistan). On May 1, 2020, a part of an earthfill dam failed along the Sardoba Reservoir, and a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. Accurate mapping and monitoring of the flooded areas are crucial for the damage assessment and the mitigation efforts. Satellite Earth Observation datasets can serve for these purposes due to their greater availability with high spatial and temporal resolutions. However, the optical sensors have limitations for data acquisition due to the atmospheric conditions, particularly the cloud cover, which often severely affects the image usability when floods occur. The synthetic aperture radar sensors provide valuable information under all weather conditions, but their interpretation is relatively difficult. Therefore, a data fusion methodology is proposed here for the integrated use of Sentinel-1 and Sentinel-2 datasets using a set of features obtained from both. Four different feature combinations were evaluated using the random forest classifier. The pre-processing steps for the feature extraction are explained in detail and the results are discussed here. The proposed algorithm exhibits very high classification accuracy for the flooded areas and flooded vegetation classes. The method can be employed for the flash flood mapping at regional scale. In addition, the damage assessment especially for agricultural areas in the region is very important for accounting the economic losses and the resilience purposes.
MULTI-TEMPORAL ANALYSIS OF LAND USE AND VEGETATION COVER IN SÃO FRANCISCO DO SUL AND ITAPOÁ, SANTA CATARINA STATE - BRAZIL: IMPLICATIONS TO GROUNDWATER CONTAMINATION
Authors: Ferreira A. C. S. J. V. , Osako L. S.
Published: June 2021

Abstract. By means of the multitemporal approach, we analyze the changes in land use and vegetation cover in the São Francisco do Sul and Itapoá municipalities, located in the northern coast of the State of Santa Catarina, Brazil. LANDSAT satellite images from 1991 to 2019 were analyzed and classified into four classes of soil use and occupation (vegetated area, bare soil, urbanized area, and water body) by the GEOBIA method. Weighted sum analysis was applied to the ‘urbanized area’ class and the data available on geologic units, so as to identify areas vulnerable to groundwater contamination. The vegetated areas of São Francisco do Sul and Itapoá have diminished along the 2000’s, but recovery measures, such as reforesting and restraint of soil exposure, have been adopted, with 89.61% success. The urban zone has increased 3.36% in the last 28 years. The achieved overall accuracy for the classification was of 79.33% and the Kappa coefficient was 0.69. The analysis of groundwater vulnerability to contamination helped identify regions more susceptible to pollution, which coincide almost entirely with those where urbanization was more intense in areas where unconsolidated sediments predominate.
SPATIO-TEMPORAL ANALYSIS OF LAND USE DYNAMICS AND ITS POTENTIAL IMPLICATIONS ON LAND SURFACE TEMPERATURE IN LAHORE DISTRICT, PUNJAB, PAKISTAN
Authors: Sohail M. , Ali S. S. F. , Fatima E. , Nawaz D. A.
Published: June 2021

Abstract. The rapid population growth and the urge in people to move to big cities for their settlement upshot in urban expansion. While stepping into the corridor of the 21st century, the utility of remote sensing and GIS techniques in various fields has made things understandable and thus enhances the ways of investigation for better decision making and management. The paper presents the Landsat Satellite series based Land Surface Temperature retrieval concerning land use/ land cover changes over Lahore District, Punjab, Pakistan. The Spatio-temporal analysis was performed from 1980–2020. We availed high-resolution Landsat and Sentinel-2 Satellite imagery to perform Normalized Difference Vegetation Index and Supervised classification. Cloud-free satellite data was acquired from June, July, or August. Data pre-processing including atmospheric and terrain corrections were performed using ERDAS Imagine. The Red, NIR, and Thermal bands were utilized for LST estimation. ArcGIS 10.22 was used for making maps, analysis, and interpretations. The Spatio-temporal analysis of LULC and LST for the area indicates a great urbanization trend over the past forty years. People are migrating from small towns and villages to the metropolitan city of Pakistan for their livelihoods, and settlements. The built-up/urban land has expanded over the period with excessive construction that has affected the Land surface temperature. The area where human activity has increased shows higher LST’s as compared to green lands. The excessive construction has taken off the agricultural land, while the River Ravi still flows with a changing course and less water table. The COVID-19 pandemic hit in 2020 put everything on lockdown had an impact on environmental restoration due to fewer emissions and human activities. The overall classification accuracy of the images yielded substantial-high Kappa statistics of 80 %, 88%, 82%, 82.41%, and 87.76% for 1980, 1990, 2000, 2010, and the 2020 images, respectively. The unplanned urbanization is leading the Lahore District to serious environmental issues and climate change impacts. The need of the hour is to properly plan and manage the area for the coming generations to have a healthier and sustainable place to breathe in.
GRAPH NEURAL NETWORK BASED MULTI-FEATURE FUSION FOR BUILDING CHANGE DETECTION
Authors: Yuan W. , Yuan X. , Fan Z. , Guo Z. , Shi X. , Gong J. , Shibasaki R.
Published: June 2021

Abstract. Building Change Detection (BCD) via multi-temporal remote sensing images is essential for various applications such as urban monitoring, urban planning, and disaster assessment. However, most building change detection approaches only extract features from different kinds of remote sensing images for change index determination, which can not determine the insignificant changes of small buildings. Given co-registered multi-temporal remote sensing images, the illumination variations and misregistration errors always lead to inaccurate change detection results. This study investigates the applicability of multi-feature fusion from both directly extract 2D features from remote sensing images and 3D features extracted by the dense image matching (DIM) generated 3D point cloud for accurate building change index generation. This paper introduces a graph neural network (GNN) based end-to-end learning framework for building change detection. The proposed framework includes feature extraction, feature fusion, and change index prediction. It starts with a pre-trained VGG-16 network as a backend and uses U-net architecture with five layers for feature map extraction. The extracted 2D features and 3D features are utilized as input into GNN based feature fusion parts. In the GNN parts, we introduce a flexible context aggregation mechanism based on attention to address the illumination variations and misregistration errors, enabling the framework to reason about the image-based texture information and depth information introduced by DIM generated 3D point cloud jointly. After that, the GNN generated affinity matrix is utilized for change index determination through a Hungarian algorithm. The experiment conducted on a dataset that covered Setagaya-Ku, Tokyo area, shows that the proposed method generated change map achieved the precision of 0.762 and the F1-score of 0.68 at pixel-level. Compared to traditional image-based change detection methods, our approach learns prior over geometrical structure information from the real 3D world, which robust to the misregistration errors. Compared to CNN based methods, the proposed method learns to fuse 2D and 3D features together to represent more comprehensive information for building change index determination. The experimental comparison results demonstrated that the proposed approach outperforms the traditional methods and CNN based methods.
RLSNAKE: A HYBRID REINFORCEMENT LEARNING APPROACH FOR ROAD DETECTION
Authors: Botteghi N. , Sirmacek B. , Ünsalan C.
Published: June 2021

Abstract. Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques.
EVALUATION OF THE SEASONAL NIGHTTIME LST-AIR TEMPERATURE DISCREPANCIES AND THEIR RELATION TO LOCAL CLIMATE ZONES (LCZ) IN STRASBOURG
Authors: Del Pozo S. , Landes T. , Nerry F. , Kastendeuch P. , Najjar G. , Philipps N. , Lagüela S.
Published: June 2021

Abstract. More and more uses and applications are being given to local climate zone (LCZ) maps, which describe the structure of the urban and semi-urban areas. Among others, it is worth highlighting its use in studies of urban heat islands (UHI), sustainability and urban energy balance. Even if the classes are well described in the literature, it is difficult to estimate the general precision of these classification maps because the classification is highly dependent of the urban typology of the city under study. However, LCZ maps represent a reference in the field of urban climatology. This research work aims to make use of these maps to explain the strong influence of LCZ classes on land surface temperature (LST) and, consequently, on air temperature (AT). This kind of investigations will help us to explain the outliers observed in previous work between LST and AT at specific locations in the city of Strasbourg for the period 2012–2019. The LST data were obtained from the thermal infrared data of both ASTER (with 90-m spatial resolution and 16-days temporal resolution) and MODIS satellite (with 1-km spatial resolution and daily revisit period). The reference ATs were obtained from different field measurement provided by a huge network of meteorological stations distributed in the city of Strasbourg. The comparison of measured ATs and remote LSTs provide the opportunity to thoroughly evaluate the relationship between these two parameters both during the day and night, for different land covers and for different times of the year. Finally, UHI maps of Strasbourg for every season are presented.
A NOVEL PROCEDURE FOR GENERATION OF SAR-DERIVED ZTD MAPS FOR WEATHER PREDICTION: APPLICATION TO SOUTH AFRICA USE CASE
Authors: Molinari M. E. , Manzoni M. , Petrushevsky N. , Guarnieri A. M. , Venuti G. , Meroni A. N. , Mascitelli A. , Parodi A.
Published: June 2021

Abstract. The knowledge of tropospheric water vapor distribution can significantly improve the accuracy of Numerical Weather Prediction (NWP) models. The present work proposes an automatic and fast procedure for generating reliable water vapor products from the synergic use of Sentinel-1 Synthetic Aperture Radar (SAR) imagery and Global Navigation Satellite System (GNSS) observations. Moreover, a compression method able to drastically reduce, without significant accuracy loss, the water vapor dataset dimension has been implemented to facilitate the sharing through cloud services. The activities have been carried in the EU H2020 TWIGA project framework, aimed at providing water vapor maps at Technology Readiness Level 7.
ACTIVE LAYER THICKNESS RETRIEVAL OVER THE QINGHAI-TIBET PLATEAU FROM 2000 TO 2020 BASED ON INSAR-MEASURED SUBSIDENCE AND MULTI-LAYER SOIL MOISTURE
Authors: Chang T. , Han J. , Li Z. , Wen Y. , Hao T. , Lu P. , Li R.
Published: June 2021

Abstract. Active layer thickness (ALT) is an important index to reflect the stability of permafrost. The retrieval of ALT based on Interferometric Synthetic Aperture Radar (InSAR) technology has been investigated recently in permafrost research. However, most of such studies are carried out in a limited extend and relatively short temporal coverage. The combination of temporal-spatial multi-layer soil moisture data and multi-temporal InSAR is a promising approach for the large-scale characterization of ALT. In this study, we employed Small Baseline Subset Interferometry (SBAS-InSAR) technology to obtain the seasonal surface deformation from radar images of Envisat and Sentinel-1 in a permafrost region of Qinghai-Tibet Plateau (QTP). We attempt to verify and calibrate the temporal-spatial multi-layer soil moisture product in combination with the in-situ data. Based on the land subsidence data and the temporal-spatial multi-layer soil moisture data, we further improve method to retrieve the ALT information. This paper describes the progress so far and point out the future work.
DISTRIBUTION AND EVOLUTION OF ICE APRONS IN A CHANGING CLIMATE IN THE MONT-BLANC MASSIF (WESTERN EUROPEAN ALPS)
Authors: Kaushik S. , Ravanel L. , Magnin F. , Yan Y. , Trouve E. , Cusicanqui D.
Published: June 2021

Abstract. Ice Apron (IA) is a poorly studied ice feature, commonly existing in all the world’s major mountain regions. This study aims to map the locations of the IAs in the Mont Blanc massif (MBM), making use of the very high-resolution optical satellite images from 2001, 2012 and 2019. 423 IAs were identified and accurately delineated in the MBM on the images from 2019, and their topographic characteristics were studied. We generated our own Digital Elevation Model (DEM) at 4 m resolution since the freely available products predominantly suffer from significant inconsistencies, especially in steep mountain areas. Results show that most IAs exist at elevations above the regional Equilibrium Line Altitude (ELA), on steep slopes, on concave surfaces, on northern and southern aspects and on the most rugged terrains. They are also commonly associated with steep slope glaciers as 85% of them occur on these glaciers’ headwalls. A comparison between 2001 and 2019 shows that IAs have lost around 29% of their area over a period of 18 years. This is significant and the rate of area loss is very alarming in comparison with the larger glacier bodies. We also studied the effect of topographic parameters on the area loss. We found that topographic factors like slope, aspect, curvature, elevation and Terrain Ruggedness Index (TRI) strongly influence the rate of area loss of IAs.
OBSERVATIONS OF CONTINUOUS INSTABILITY FOR SCAR INLET ICE SHELF, ANTARCTIC PENINSULA
Authors: Li Y. , Qiao G. , Yuan X.
Published: June 2021

Abstract. Observation of the evolving instability of ice shelves plays a very important role in global change research. Following the suddenly large-scale collapse of the Larsen B Ice Shelf in the Antarctic Peninsula in 2002, the evolving instability for its remnant, the Scar Inlet Ice Shelf, began to be increasingly studied to provide a deeper understanding of the disintegration of the Larsen B Ice Shelf in 2002 and also provide a chance for studying the response of ice shelves to the large-scale collapse events. In this study, based on sequential Landsat images spanning 2005–2020, we produced detailed maps of the ice velocity fields for the Scar Inlet Ice Shelf. The results indicate that the ice velocities for the Scar Inlet Ice Shelf region have substantially increased since 2005, the maximum ice velocity reached more than 900 m/y in the ice shelf front. Surface rifts have also substantially increased in both length and width and are moving seawards. The ice front position of the Scar Inlet Ice Shelf is relatively stable in 2008–2010 and then steadily advancing after 2010. The acceleration of ice velocities, the dynamic change of the ice front, the increase of major surface rifts and the newly added rifts in the central part of the ice shelf, and the heavily enhanced surface crevasses are all revealing the evolving instability of the Scar Inlet Ice Shelf.
COMPARISON OF CRYOSAT-2 AND ICESAT-2 ON WATER LEVEL MONITORING OF NAM CO LAKE
Authors: Zhao H. , Xu R. , Qiao G.
Published: June 2021

Abstract. There are more than 1,000 lakes (> 1 km2) on the Tibetan Plateau and lake level is an important physical feature of lake changes. Lake level change is an important indicator to reflect changes of climate and environment in a certain area. The development of satellite altimetry has provided data support for the monitoring of lake level and effectively compensated for the deficiencies of traditional water level monitoring in alpine regions. In this study, the laser altimeter of ICESat-2 and the radar altimeter of CryoSat-2 are used to provide lake level of the Nam Co lake during the period of 2010–2020. The result showed that the standard deviation (SD) of ICESat-2 (0.0895 m) was lower than the SD of CryoSat-2 (0.2556 m) and the months with higher SD values were mostly during the ice period of Nam Co lake. ICESat-2 had a considerably decreased measurement uncertainty. There are systematic differences in lake levels extracted by different altimetry satellites and the mean bias between ICESat-2 and CryoSat-2 was around 0.45 m. After removing inter-altimeter biases, the continuous lake levels from 2010 to 2020 were constructed. The inter-annual changes in lake levels were flat or even slightly decreased and the lake level has dropped by about 0.80 m in general. The water level generally reached the highest from September to October of the year in terms of intra-annual changes. Besides, temperature and precipitation changes were closely related to lake level tendency.
DETERMINATION OF REGIONS SUITABLE FOR AGRICULTURE IN THE GORDON COSENS FOREST OF ONTARIO BY MEANS OF ANALYTICAL HIERARCHY PROCESS WITH FUZZY LOGIC INFERENCE
Authors: Pittman R. , Hu B. , Sohn G.
Published: June 2021

Abstract. Analytical Hierarchy Process (AHP) with fuzzy logic inference on attributes was employed to determine areas most suitable for agriculture in the Gordon Cosens Forest (GCF) region within the District of Cochrane in northern Ontario, Canada. Attribute layers considered were soil texture, ELC (Ecological Land Classification) moisture regime, slope, canopy height model (CHM), distance to existing road networks and distance to water bodies. Fuzzy logic inference was utilized to rescale the attributes to a normalized range, taking into account preferability, which was then subjected to pairwise comparisons via AHP to determine the attribute layers' weightings. For the study area, the localities identified as most compatible for agricultural development include the southeastern section of the GCF at approximately 30 km south of the community of Fauquier and the westernmost area of the GCF at about 10 km east of Mattice.
AN INVERSION APPROACH FOR BIOCHEMICAL PARAMETERS OF VEGETATION BASED ON THE PROSPECT-5 MODEL
Authors: Xing X. , Zheng X. , Liu J.
Published: June 2021

Abstract. Accurate inversion of vegetation biochemicals using the PROSPECT model mostly depends on a proper inversion approach, including a suitable optimizing algorithm, appropriate dependent variables, and different properties from spectra of reflectance (R) and transmittance (T). In this paper, we propose a special inversion method using PROSPECT-5 and then explore its effectiveness in inverting chlorophyll, carotenoids, equivalent water thickness, and dry matter per area data from the ANGERS database. The inversion strategy includes (i) an optimal algorithm with constrained bounds (fminsearchbnd) to replace the common function fminsearch, (ii) and four parameters are considered together and separately as dependent variables of models, (iii) Using properties from the spectra of R, T and combined R&T to invert the above four biochemical parameters. The results show that fminsearchbnd can improve the model's R2 based on a field-measured database. Moreover, using the entire set of parameters together as the model inputs is more effective than using single parameters separately. T spectra are favoured for all parameter inversions in the model database while being inapplicable in the ANGERS database. These findings provide an appropriate inversion strategy for the PROSPECT-5 model in vegetation biochemical parameters analysis and suggest further research to develop an accurate inversion process for vegetation based on various physical models.
PREFACE: TECHNICAL COMMISSION III
Authors: Jiang J. , Shaker A. , Zhang H.
Published: June 2021

Abstract. The work of ISPRS Technical Commission III is devoted to remote sensing. For the XXIVth ISPRS CONGRESS – 2021 Edition (Digital), Technical Commission III received total 201 submissions, including 72 full papers and 138 abstracts. Among these submissions 38 are accepted as peer-reviewed contributions for publication in the ISPRS Annals, 118 were accepted for publication in the ISPRS Archives.These papers are dedicated mostly to topics of the 10 TC III working groups and 4 inter-commission working groups as follows – WG III/1: Thematic Information Extraction; WG III/2: Microwave Remote Sensing; WG III/3: SAR-based Surface Generation and Deformation Monitoring; WG III/4: Hyperspectral Image Processing; WG III/5: Information Extraction from LiDAR Intensity Data; WG III/6: Remote Sensing Data Fusion; WG III/7: Landuse and Landcover Change Detection; WG III/8: Remote Sensing of Atmospheric Environment; WG III/9: Cryosphere and Hydrosphere; WG III/10: Agriculture and Natural Ecosystems Modelling and Monitoring; ICWG III/II: Planetary Remote Sensing and Mapping; ICWG III/Iva: Disaster Assessment, Monitoring and Management; ICWG III/IVb: Remote Sensing Data Quality; ICWG III/IVc: Environment and Health. The papers and abstracts were evaluated by the experts in the field and Working Group Chairs according to content, significance, originality, relevance, and clearness of presentation.We would like to thank the authors for their contributions, the reviewers for their reviewing, the working group officers for their efforts on calling for papers, and the organizers of the Congress for publishing this volume.
AERIAL PHOTOGRAMMETRY AND MACHINE LEARNING BASED REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR AN EARTHQUAKE PRONE AREA IN TURKEY
Authors: Karakas G. , Kocaman S. , Gokceoglu C.
Published: June 2021

Abstract. Landslide is a frequently observed natural phenomenon and a geohazard with destructive effects on economies, society and the environment. Production of up-to-date landslide susceptibility (LS) maps is an essential process for landslide hazard mitigation. Obtaining up-to-date and accurate data for the production of LS maps is also important and this task can be achieved by using aerial photogrammetric techniques, which can produce geospatial data with high resolution. The produced geospatial datasets can be integrated in data-driven methods for obtaining accurate LS maps. In the present study, LS map was produced by using data-driven machine learning (ML) methods, i.e. random forest (RF). An earthquake and landslide prone area from the south-eastern part of Turkey was selected as the study area. Topographical derivatives were extracted from digital surface models (DSMs) produced by using aerial photogrammetric datasets with 30 cm ground sampling distances. The lithological parameters were employed in the study together with an accurate landslide inventory, which were also delineated by using the high-resolution DSMs and orthophotos. The relationships between the landslide occurrence and the pre-defined conditioning factors were analyzed using the frequency ratio (FR) method. The results show that the RF method exhibits high prediction performance in the study area with an area under curve (AUC) value of 0.92.
GEOMORPHOLOGICAL MAPPING OF INTERTIDAL AREAS
Authors: Lu M. , Groeneveld L. , Karssenberg D. , Ji S. , Jentink R. , Paree E. , Addink E.
Published: June 2021

Abstract. Spatiotemporal geomorphological mapping of intertidal areas is essential for understanding system dynamics and provides information for ecological conservation and management. Mapping the geomorphology of intertidal areas is very challenging mainly because spectral differences are oftentimes relatively small while transitions between geomorphological units are oftentimes gradual. Also, the intertidal areas are highly dynamic. Considerable challenges are to distinguish between different types of tidal flats, specifically, low and high dynamic shoal flats, sandy and silty low dynamic flats, and mega-ripple areas. In this study, we harness machine learning methods and compare between machine learning methods using features calculated in classical Object-Based Image Analysis (OBIA) vs. end-to-end deep convolutional neural networks that derive features directly from imagery, in automated geomorphological mapping. This study expects to gain us an in-depth understanding of features that contribute to tidal area classification and greatly improve the automation and prediction accuracy. We emphasise model interpretability and knowledge mining. By comparing and combing object-based and deep learning-based models, this study contributes to the development and integration of both methodology domains for semantic segmentation.
EXPLORING THE USE OF CLASSIFICATION UNCERTAINTY TO IMPROVE CLASSIFICATION ACCURACY
Authors: Moraes D. , Benevides P. , Moreira F. D. , Costa H. , Caetano M.
Published: June 2021

Abstract. Supervised classification of remotely sensed images has been widely used to map land cover and land use. Since the performance of supervised methods depends on the quality of the training data, it is essential to develop methods to generate an enhanced training dataset. Active learning represents an alternative for such purpose as it proposes to create a dataset of optimized samples, normally collected based on classification uncertainty. However, it is heavily dependent on human interaction, since the user has to label selected samples over a number of iterations. In this paper, we explore the use of uncertainty to improve classification accuracy through a single iteration. We conducted experiments in a region of Portugal (Trás-os-Montes), using multi-temporal Sentinel-2 images. The proposed approach consisted in computing the classification uncertainty of a Random Forest to collect additional training data from areas of high uncertainty and perform a new classification. An accuracy assessment was performed to compare the overall accuracy of the initial and new classifications. The results exhibited an increase in accuracy, though considered not statistically significant. Obstacles related to labelling additional sampling units resulted in a lack of additional training data for various classes, which might have limited the accuracy improvement. Additionally, an uneven proportion of additional training sampling units per class and the collection of new sample data from a limited number of uncertainty regions might also have prevented a higher increase in accuracy. Nevertheless, visual inspection of the maps revealed that the new classification reduced the confusion between some classes.
SPATIAL RESLUTION SENSITIVITY ANALYSIS OF CLASSIFCIATION OF SENTINEL-2 IMAGES BY PRE-TRAINED DEEP MODELS FROM BIG EARTH NET DATABASE
Authors: Naali F. , Alipour-Fard T. , Arefi H.
Published: June 2021

Abstract. Classifying and monitoring different vegetation types is important for forest management, food resources, and assessing the potential impacts of climate change. In this regard, several methods have been developed to study them using remote sensing data, and with the advent of neural networks, new methods are being proposed, especially in the field of automatic land use classification. In this research, multispectral Sentinel-2 satellite image has been used due to having spectral information and different spatial resolution for classifying plant species. Deep learning models have the ability to learn and recognize different features of images, but require a large number of training samples, so we used pre-trained ResNet networks with depths of 50, 101 and 152 layers, that trained with BigEarthNet dataset. The main purpose of this study is to evaluate the sensitivity of ResNet networks to spatial resolution. Results show that ResNet 101 was more stable than other networks, and the Resent 50 with an overall accuracy of 76.2 has the highest accuracy at a resolution of 20 meters.
Currents Generated by the Sea Breeze in the Southern Caspian Sea
Authors: Masoud Mina , Pawlowicz Rich
Published: June 2021

Abstract. The sea breeze system is the dominant atmospheric forcing at high frequency in the southern Caspian Sea. Here, we describe and interpret current meter observations on the continental margins of the southern Caspian from 2013 to 2014 to identify and characterize the water column's response to the sea breeze system. Time series analysis provides evidence for diurnal baroclinic current signals of O (0.02 m s−1) and surface height changes of O (0.03 m). A two-layer model, including interfacial and bottom friction is developed to further investigate the sea breeze response. This model is able to reproduce the structure, amplitudes, and phases of observed diurnal current fluctuations, explaining half of the variance in observational current response at frequencies at 1 cpd and higher. The sea breeze response thus results in a “tide-like” daily cycle which is actually linked to the local forcing all along the southern Caspian coast.
The relationship between dental fear and cooperation of children during dental treatments with their parents’ general health
Authors: Daneshvar Seyedeh Hediyeh , Azizi Saeedeh
Published: June 2021

Objective: Dental fear is a common phenomenon in children and parents can have a key role in the development of child's dental anxiety. There is moderate evidence to support the relationship between the parental general health and children’s dental fear. The aim of this study was to investigate the relationship between dental fear and cooperation of children during dental treatment with the general health of parents.Methods: This cross-sectional descriptive study was done on 130 children aged 6 to 12 at the Department of Pediatric Dentistry, Guilan University of Medical Sciences from May to September 2020.The personality traits and general health of parents were assessed by using the General Health Questionnaire (GHQ-28). The Frankl's behavior rating scale and Children's Fear Survey Schedule Dental Subscale (CFSS-DS) were used to respectively assess the degree of cooperation during dental practices and children’s dental fear. Statistical approaches included T-test, Chi-square and Pearson Linear correlation. A significant level of differences was taken as P<0.05.Results: 69.3 % of children behaved positively and definitely positively during dental treatment. 56 boys and 74 girls with mean age of 9.13 ±2.02 years participated in this study. There was a significant correlation between the children's age with dental fear and cooperation level (P<0.001). There was no significant difference in dental fear score (P=0.63) and cooperation level (P=0.99) between boys and girls. There was not statistically significant relationship between the general health of the parents and the level of children's cooperation and dental fear during dental treatment (P>0.001).Conclusion: There was not relationship between parent's general health with children's dental fear and cooperation.
In the PaLRaP Spotlight: Michael Lear
Authors: Campbell Stephanie
Published: June 2021

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Estudio de sentencias de la custodia de menores tras la ruptura de la pareja. ¿Custodia versus bienestar?
Authors: López Viso Mónica , Pedrosa Gil Lucía
Published: June 2021

La presente investigación tiene por objeto el análisis de la guarda y custodia y trata de ahondar en los aspectos sociales más significativos. A partir del caso específico de la provincia de Ourense (Galicia, España) se han abordado las principales consecuencias familiares, económicas y sociales de la ruptura de una pareja con hijas e hijos. Para ello, se ha realizado un análisis comparativo de las sentencias judiciales de la Audiencia Provincial de Ourense en un periodo de diez años. Los datos indican que las custodias se otorgan de forma mayoritaria y exclusiva a la madre. Este escenario presenta un nuevo tipo de unidad familiar, tras la ruptura de la pareja, monomarental en su mayoría, formados por una mujer sola con sus hijas e hijos que reciben, y perciben, unas escasas cuantías como pensión de alimentos.
Distribution of Power between the Centre and Provinces in Pakistan under the Constitution of 1973
Authors: Akram Sidra , Azhar Dr. Muhammad
Published: June 2021

Centre-Province relationship and the distribution of power between centre and provinces is a complex and multifaceted sort of administrative and political debate for all around the world including Pakistan. Pakistan is based on federation where power has been divided between the central government and their provinces. Pakistan, which is partitioned into four regions, those are performing overwhelming participation in the organization and armed force. The other three units have demonstrated their discontent over the portion of forces between the government and areas and raised their voice for more independence and political shields. This paper will analyze the concept of a federation which considered appropriate for varied political societies to promote coordination and cooperation in the political, social, administrative, and economic sphere. So that it gives the fragile balance to conflicting demands between the centre and their provinces. This paper will discuss the fact, that the constitution of 1973 provides the suitable structural arrangement in Pakistan for union and its unit, but could not alter the power mechanism of the centre. This paper will discuss the prevailing debate of the centre-province relationship and quantum of power-sharing among them
Honor Killing: A case study of Pakistan
Authors: Kanwal Shagufta
Published: June 2021

Pakistan along with many other parts of the world has the persistent phenomenon regarding Honor Killing. This research article states the case study of Pakistan in relation to honor killing, its causes and the struggles made by national and international community to reduce such like honor crimes. The effects of several amendments that are previously made have been discussed in the piece of paper stating therein that all the amendments are unable to tackle the deficiencies. There is an actual desire of further improvements and latest amendments that is required to be resolved to eliminate the issues in hand. In the last section of the paper, it is addressed that how Pakistan has made up with the international obligation and its compliance towards the protection of women rights. Analyses of the elements that are keeping the practice of honor killing as well as the recommendation to amend the relevant laws are explained in this research article.
Historic Conjunction of Juvenile Law and Child Psychology
Authors: Hassan Dr. Abida , Saeed Muhammad Arif
Published: June 2021

In any developed nation, the way law is implemented is a seen as a reflection of what the statute or article was intended for, since modern society is a complex blend of different societal layers, it is necessary to make distinctions based on certain criteria in order to satisfy the ulterior motive of law: to maintain peace and harmony in society. One such distinction is that between the law applicable to adults and that applicable to minors, or in legal terms, juveniles. Pakistan also shared its history of juvenile justice with India, up until its independence in 1947. Even then it took Pakistan fifty-three years to formulate a uniform piece of legislation for juveniles, and then another eighteen to update and revise it considerably. By virtue of the procedure of independence, Pakistan inherited a lot of laws that had been introduced by the British in the subcontinent during their rule. Several of these laws were oriented towards juveniles and the need for their separation from adult, either wholly or partially and some of them survive to this day as well.