GISTAM 2024 Abstracts

Area 1 - Data Acquisition and Processing

Full Papers

Paper Nr:	43
Title:	UAV-Based Analysis of Armour Rock Granulometry and Hydraulic Stability
Authors:	Alisson Villca, Muhammad A. Sammuneh, Poupardin Adrien, Jena Jeong, Rani El Meouche and Georges Chapalain
Abstract:	Dikes worldwide play a crucial role in mitigating flooding risks. Often, armour rocks are placed within the wave impact zone to protect the dike from wave loading. To ensure a dike is in optimal condition the assessment of the hydraulic stability of armour rocks is necessary. This study presents a Granulometric analysis technique, which is based on UAV photogrammetry and optical digital granulometry to evaluate the spatial distribution and possible variations in time of armour rocks granulometry and hydraulic stability. This is a new non-invasive technique with which spatial, temporal changes can be studied. Our study area is located in Camargue, south of France. This low-laying region, exposed to multiple storms, is among the most endangered zones by sea level rise. We concluded that monitoring of the dike is possible using this technology the optical granulometric analysis could be performed on UAV images. We conducted granulometry distribution calculations for armour rocks, even when they were covered with moss. Our findings show the spatial variation of granulometry along the dike. In specific areas of interest where hydraulic stability was assessed, based on the granulometry results, we have found areas with low hydraulic stability that need to be investigated more thoroughly.
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Short Papers

Paper Nr:	46
Title:	Assessing the Risks of Enhancing the Current Europe’s ADA Web Map with Ground Movement Classification Data
Authors:	José A. Navarro, Anna Barra and María Cuevas-González
Abstract:	The European Ground Motion Service is offering data on ground movement across Europe with millimetre precision. With the intention of helping in the interpretation of such a large volume of data, the CTTC has already created an online Active Deformation Areas (ADA) web map, which can be consulted freely. The CTTC is considering the possibility of enhancing the said web map by including the causes explaining why ADAs occur. This article presents the changes in the self-developed ADAtools to make possible such enhancement and analysis of the impacts on the current implementation of the web map as well as an early assessment of the risks that such changes would imply.
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Area 2 - Interaction with Spatial-Temporal Information

Full Papers

Paper Nr:	11
Title:	Derivation of Critical Infrastructure Accessibility Index Using GIS-MCDA and Network Analysis: Case Study of Sarajevo
Authors:	Ivan Marić, Aida Avdić and Boris Avdić
Abstract:	This study explores the accessibility of critical infrastructures (CRITIS) in urban planning, focusing on the City of Sarajevo. CRITIS, essential for societal functioning, encompasses diverse services vital to social, economic, political, health, educational, and administrative systems. The authors leverage geographic information system (GIS) tools to construct an accessibility model for Sarajevo, analysing the spatial availability of critical functions. Six groups of CRITIS indicators, composed of 29 CRITIS elements, were used in the derivation of critical infrastructure accessibility index. The methodological framework was based on implementation of network GIS analysis, interpolation method (IDW) and GIS multi-criteria analysis, which could be applicable to similar research studies. Local communities concentrated in the strict urban core (Ferhadija, Baščaršija) have the best accessibility of CRITIS, while peripheral local communities with a large area, such as Mošćanica and Reljevo, have the lowest. Results suggest a zonal categorization of the urban area, providing valuable insights for spatial planning and future urban development management. The study reveals that the highest value of CRITIS accessibility doesn’t necessarily align with the most densely populated areas at local community level.
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Short Papers

Paper Nr:	24
Title:	Dynamic Exposure Visualization of Air Quality Data with Augmented Reality
Authors:	Sylvain Renault, Ingo Feldmann, Lieven Raes, Jurgen Silence and Oliver Schreer
Abstract:	Increased awareness regarding air pollution and environmental conditions is more relevant than ever. Notably, there is already widespread availability of do-it-yourself (DIY) environmental and air quality sensors across Europe. These data are accessible to the public through various web interfaces, providing insights into current and past environmental conditions across different regions of Europe. Augmented Reality (AR) stands out as a promising technology to enable citizens to monitor environmental conditions in their communities and comprehend their own impact, thus motivating behavioural changes. Nevertheless, effectively visualizing real-time environmental data in the 3D AR space remains a challenge. Innovative visualization techniques are necessary to present environmental data in a clear and engaging manner. In this paper, we introduce a framework, a visualization concept, and a prototype AR application capable of providing a visual overlay of environmental information such as air quality or traffic data. These concepts stem from the European project H2020 COMPAIR, which involved users throughout the development process. The application will undergo evaluation in various pilot cities and regions and will be publicly available via app stores by mid-2024.
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Paper Nr:	57
Title:	An Augmented Reality System Architecture for Flood Management
Authors:	Alexios Stavroulakis, Despina Dimelli, Michail Roumeliotis and Aikaterini Mania
Abstract:	Flooding represents a considerable danger to both human lives and possessions, rendering it a prevalent natural hazard. Navigation and risk assessment methods, in dynamically changing flood environments, are dependent on flood visualization methods. Addressing the limitations of 2D visualization as well as Virtual Reality (VR) setups that are employed in off-site simulations, this paper presents the system architecture of a novel system for real-time urban flood management utilizing head-worn AR, integrating extreme scale data. Our system architecture will offer a depiction of urban flood inundation in the city of Dortmund, Germany, dynamically visualizing potential evacuation routes and water levels, on-site, in urban areas, while rescuers are in operation. The system’s integration with large-scale data analytics will allow the dynamic combination of weather forecasts, sensor networks, historical flood data and urban topography.
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Paper Nr:	39
Title:	Evaluating the Urban Parks Cooling Extent Using Satellite Observations: An Alternative Approach
Authors:	Tesfaye Tessema, Dale Mortimer and Fabio Tosti
Abstract:	Green infrastructure is the cooling hub of the built environment in urban settings. These interactions could contribute towards the reduction of the rise in temperature due to urban heat island effects. It is common practice to evaluate the cooling extent using onsite observations. Alternatively, satellite data could be a possible source to perform the former. We analyse and evaluate the extent using the satellite observations. Intuitively, as we go further from a park, the cooling effect will decrease but this need to be quantified. We analyse Landsat-8 images to generate a temperature distribution in urban environment. The Land Surface Temperature (LST) was derived from Landsat-8 and downscaled from 30 m to 10 m using the Sentinel-2 spectral indices in the Greater London area. This gives a relatively high resolution LST variation in urban environment. A profile over a park was extracted to observe the extent of cooling from the green infrastructure extends towards the build environment. The cooling effect varies with the park and the effect extends up to 300 m. These observations contribute towards the urban planners to maximise the cooling benefits of urban parks to promote urban resilience and sustainability.
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Area 3 - Spatial Data Mining

Short Papers

Paper Nr:	40
Title:	Semantic Segmentation of Paddy Parcels Using Deep Neural Networks Based on DeepLabV3
Authors:	Syazwani Basir, Nurul A. Aziz and Nurshafiza Z. Abiddin
Abstract:	Paddy parcels are frequently converted to other structures which contributes significantly to changes in paddy cultivation areas and a decrease in rice production. Determining the current land use status for paddy parcels annually is quite challenging; thus, the Paddy Geospatial Information System (MakGeoPadi) has been developed to determine the precise Malaysian paddy cultivation regions in order to provide a sufficient food supply for the entire country. Deep convolutional neural network (DCNN) algorithms such as DeepLabV3 are used in this study to accurately estimate paddy yield of 12 granaries. The objective of this study is to enhance the DeepLabV3 paddy parcel detection model to generate data that can be relied upon for reliable decision-making. Deep-learning applications based on the DeepLabV3 model were classified into four classes: active paddy parcel (PA), miscellaneous paddy parcel (PP), permanent structures (SK) and permanent crop (TK) using ResNet50 in ArcGIS Pro version 2.9. DCNN has been utilised to perform semantic segmentation. The DCNN architecture known as DeepLabV3 is trained using the 16,000 datasets in the experiment, with Pleiades satellite images scaled at 224 x 224-pixel sizes. Following the training phase, the DeepLabV3 model achieved the highest successful training accuracy, scoring 91.6%.
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Paper Nr:	28
Title:	Automated Georeferencing and Extraction of Building Footprints from Remotely Sensed Imagery using Deep Learning
Authors:	Aniruddha Khatua, Apratim Bhattacharya and Bharath H. Aithal
Abstract:	Extracting building footprints from remotely sensed photos is crucial in conducting analyses in domains such as land-use analysis, transportation planning and development, environmental studies, and others. Various methodologies and strategies have been suggested for extracting building footprints from satellite or UAV images, aiming to circumvent the arduous, time-consuming, less effective, and costly process of manually digitizing building footprints. These proposed methodologies and strategies have demonstrated their efficacy in detecting and extracting features. However, they do not adequately retain the geographical information during the output generation process. This paper presents a pipeline that can automatically extract geographical information from input photos and transfer it to the output image, thereby achieving automated georeferencing of the output image. The pipeline utilizes the YOLOV8 model, an advanced deep-learning-based architecture for object detection and segmentation. The detection and segmentation findings, combined with the acquired geographical information, are used to perform vectorization and generate vector images of the extracted building footprint. This suggested pipeline streamlines the process of obtaining building footprint data linked to geospatial information by automating the georeferencing and shapefile preparation phases, reducing the associated complications. This automation not only expedites the process but also improves the precision and uniformity of the output datasets.
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Area 4 - Remote Sensing

Full Papers

Paper Nr:	47
Title:	Improved Analysis of EGMS Data for Displacement Monitoring: The Case Study of Regina Montis Regalis Basilica in Vicoforte, Italy
Authors:	Davide Lodigiani, Marica Franzini and Vittorio Casella
Abstract:	The Basilica of Vicoforte has always interested geotechnical engineers due to its location in a geologically complex area. One part of the Basilica is built on marl, while the other is built on clay. These two types of soil have different mechanical properties, which have caused the Basilica to experience various foundation failures over time. Monitoring is necessary to evaluate structural evolution and prevent further damage. Radar images are one of the geomatics techniques that can be used to perform these types of analyses; however, SAR data processing is challenging and requires specialized skills and software to monitor deformations using the PSInSAR approach. The European Ground Motion Service (EGMS) is useful for users and researchers, but analyzing specific buildings or monuments requires a more refined grid. The paper proposes a package of codes implemented using MATLAB release 2023b to manage grid spacing flexibly, customize it according to structure dimensions, and manage potential blunders. After a thorough data analysis, it was concluded that the monument exhibits no signs of subsidence trends. Instead, the analysis revealed that it undergoes seasonal fluctuations closely associated with temperature changes. The proposed approach enhances data accuracy and reliability, resulting in valuable insights and informed decisions.
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Paper Nr:	49
Title:	Estimation of Height of Building Using High Resolution Satellite Image
Authors:	Nitin L. Gavankar, Ravindra R. Rathod and Vivek N. Waghmare
Abstract:	Buildings along with their properties such as, shape, rooftop reflectance, structure, etc. are one of the most commonly observed structures in urban areas. Two-dimensional (2D) building footprint along with building height information play an important role in the field of urban development, urban planning, population estimation, map making, disaster management, and various other socioeconomic applications. Shadow cast by buildings plays a vital role in estimation of height of building. In this study, sun-satellite geometry method using shadow cast by building has been used to estimate height of building. However, accurate shadow detection, extraction, and measuring width of the shadow zone are some of the important aspects in estimation of height of building. In order to detect and extract width of the shadow zone accurately, OBIA has been used. Further, accuracy in measuring width of the shadow zone has been improved by introducing a new algorithm and considering sun illumination direction and orientation of building. OBIA along with new algorithm to measure width of the shadow zone provides a sound methodology for estimation of height of all types and shape of buildings.
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Short Papers

Paper Nr:	10
Title:	Prediction of Turbidity and TDS in Dam Reservoir from Multispectral UAV-Drone and Sentinel-2 Image Sensors Using Machine Learning Models
Authors:	Yashon O. Ouma, Phillimon Odirile, Boipuso Nkwae, Ditiro Moalafhi, George Anderson, Bhagabat P. Parida and Jiaguo Qi
Abstract:	This study presents results on the utility of DJI P4 Multispectral (DJI-PH4) UAV-Drone and Sentinel-2 MSI (S2-MSI) satellite datasets for the retrieval of Turbidity and Total Dissolved Solids (TDS) using empirical linear regression (ELR), XGBoost (eXtreme Gradient Boosting) and Random Forest Regression (RFR) machine learning (ML) models. For the case study of Gaborone dam in Botswana, 21 water sampling points were correlated with the corresponding spectral reflectances from DJI-PH4 and S2-MSI imagery. For the estimation of Turbidity, XGBoost gave the best prediction results with average training accuracy of R2 = NSE = 0.999, MAE=0.001 NTU, RMSE = 0.001 NTU and PBIAS = 0.1% for both the DJI-PH4 and S2-MSI sensors. XGBoost performed better than ELR and RFR at the model training phases, however its prediction of Turbidity in testing was lower than ELR but nearly same as RFR. In predicting TDS from both sensors, XGBoost had the highest performance with equivalent accuracy measures as for the prediction of Turbidity. Both the training and testing results for the estimation of TDS is accurate from the sensors, with ELR marginally outperforming the XGBoost and RFR in the testing phase with R2 = 0.998, MAE=0.338 mg/L, RMSE = 0.435 mg/L and NSE = 0.858. For the prediction of Turbidity, all the ML models gave good training results from the drone and Sentinel-2 data except for RFR in the case of Sentinel-2. The introduction of ensemble ELR-XGBoost model significantly improved the prediction of the water quality parameters from the drone and Sentinel-2 datasets. With the potential of providing high-frequency and large spatial coverage observational data in the near-real-time mode, the results of this study demonstrate the applicability of UAVdrone for the retrieval of Turbidity and TDS physical water quality parameter in dam reservoirs.
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Paper Nr:	23
Title:	Seasonal Water Quality Assessment Using Remote Sensing in Al Rafisah Dam, United Arab Emirates
Authors:	Afra A. Alserkal, Amel A. Alblooshi and Rami Al-Ruzouq
Abstract:	Water bodies differ in their chemical, biological and physical properties. These properties determine their quality, and sequentially, their applications. Conventional surface water quality analysis involves timely, costly, and intensive field and laboratory work. Remote sensing coupled with a geographic information management system (GIS) can offer an alternative to estimating water quality in remote or inaccessible locations. The main objectives of this study are to estimate chlorophyll-a, colour dissolved organic matter (CDOM), total suspended matter (TSM) and turbidity using remote sensing methods and to display them in temporal distribution maps. In this research, quantitative methodology was used to calculate the four water quality parameters using Sentinel-2 images of the Al Rafisah Dam in Sharjah, United Arab Emirates during the months of February, April, August and December of 2021. The Case 2 Regional Coast Colour (C2RCC) processor in the Sentinel Application Platform (SNAP) developed the equations and performed the calculations for chl-a, CDOM and TSM. ArcGIS Pro software was used for estimating turbidity with the normalized difference turbidity index (NDTI), as well as creating the spatio-temporal distribution maps. Overall comparative evaluation of the concentration patterns showed that the parameters selected for the study are interrelated, yet may vary depending on seasonal variations and human activities. Water quality research using remote sensing and GIS plays an important role in encouraging researchers to conduct more studies in unattainable sites or understudied areas such as the Al Rafisah Dam.
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Paper Nr:	37
Title:	Studying Seismic Events via Satellite Interferometry for the Territory of the Balkan Peninsula
Authors:	Mila Atanasova-Zlatareva and Hristo Nikolov
Abstract:	The prime focus of the current article is to present a pilot study in the project "Study of co-seismic deformations of the Earth’s crust for the territory of the Balkan Peninsula based on satellite data" that started in December 2023. The purpose of the project is the regular monitoring of co-seismic deformations of the Earth’s crust using innovative methods for processing remotely sensed data. The main task is to demonstrate the operational readiness to determine the magnitude of the deformations of the earth’s surface, the size of the affected areas and to prepare maps of the displacements that have occurred. This goal will be achieved through the creation and realization of a methodology for extracting high-quality information from SAR products aimed at continuous monitoring of areas that could be considered as potential foci of strong earthquakes, integrating information from interferometric images and GNSS observations. As a result, a working prototype of an information system for monitoring and prevention of the consequences of co-seismic deformation of the earth’s crust (landslides, collapses, etc.) based on freely available data provided by ESA and national agencies will be created. The core of this system is an archive that will be created with data from satellite SAR instruments for regions of the Balkans overlap in area and time registered earthquakes with a magnitude above 5.0 for the period 2015-2025. The expected results are the created deformation maps that, comparable to the position of the faults in the area and analysed with the tectonic setting.
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Paper Nr:	41
Title:	Assessment of Census and Remote Sensing Data to Monitor Irrigated Agriculture in Mexico
Authors:	Jean-Francois Mas and Azucena Pérez-Vega
Abstract:	Irrigated agriculture faces imminent threats, such as escalating water scarcity and climate change impacts. Water scarcity is particularly crucial in countries such as Mexico, where approximately 41% of the land comprises arid and semi-arid zones. The study assesses the quality and consistency of monitoring irrigated agriculture in a municipality of the State of Guanajuato in central Mexico using agricultural census information and advanced remote sensing data from Landsat 8, MODIS, and ECOSTRESS. Preliminary analyses showcase the dominance of wheat and barley crops in Pénjamo, with MODIS time series effectively capturing crop growth dynamics. The study highlights the potential of remote sensing in estimating irrigated crop dynamics proportions and the associated water consumption at different scales.
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Paper Nr:	44
Title:	Semantic Segmentation of Crops via Hyperspectral PRISMA Satellite Images
Authors:	Manilo Monaco, Angela Sileo, Diana Orlandi, Maria L. Battagliere, Laura Candela, Mario G. Cimino, Gaetano A. Vivaldi and Vincenzo Giannico
Abstract:	Data from hyperspectral remote sensing are promising to extract and classify crop characteristics, because it provides accurate and continuous spectral signatures of crops. This paper focuses on data acquired by PRISMA, a high-resolution hyperspectral imaging satellite. Due to this large data availability, huge training datasets can be built to feed modern deep learning algorithms. This paper shows a spectral-temporal data processing based on random forest to perform feature selection, and on two-dimensional convolutional neural network to carry out classification of crops, exploiting variations in respective phenological phases during the annual life cycle. The proposed solution is described via a pilot case study, involving a field farmed with olive groves and vineyards in Apulia, Italy. Moreover, one-dimensional convolutional neural networks are used to compare classification accuracies. Early results are promising with respect to the literature.
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Paper Nr:	19
Title:	Oil Spill Detection Using Remote Sensing and GIS in Eastern Coast of United Arab Emirates
Authors:	Afra Al Teneiji, Aaesha Al Mesafri and Rami Al-Ruzouq
Abstract:	One of the most dangerous oceanic pollutions nowadays is marine oil spills, which occur when crude oil is released into the oceanic water and must be contained quickly since they can extend to large areas and result in serious ecological, economic, and health consequences. Places like the Gulf of Oman are highly vulnerable to oil spill accidents due to the high marine activity there. Remote sensing and geographic information systems (GIS) have proven their capabilities in countless fields, and detecting oil spills is one of them. This study explores the potential of combining remote sensing and Geographic Information Systems (GIS) for oil spill detection on the Eastern Coast of the United Arab Emirates. Leveraging Sentinel-1 SAR and Sentinel-2 optical data, we develop and evaluate a methodology to identify oil spills. While specific accuracy assessments await further testing, initial visual analysis indicates promising results. The study contributes to advancements in oil spill detection by demonstrating the potential of using these remote sensing techniques in this region. Additionally, it highlights the value of GIS integration for data analysis and visualization. This research holds promise for improved oil spill monitoring and environmental protection efforts on the Eastern Coast of the United Arab Emirates.
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Area 5 - Modeling, Representation and Visualization

Short Papers

Paper Nr:	35
Title:	Geodetic Fundamentals in the Development of a Voxel Model for the Subsoil of the City of Sevilla (Spain)
Authors:	Andreas Fuls, Emilio J. Mascort-Albea, Francisco M. Hidalgo-Sánchez, Martin Kada, Cristina Soriano-Cuesta, Rocío Romero-Hernández and Antonio Jaramillo-Morilla
Abstract:	Current global challenges require a better understanding of the subsoil to optimise underground resources and plan for sustainable development. This is a key issue in anthropised metropolitan environments, where the high density of elements makes difficult to gain knowledge of this reality. The use of Geographic Information Systems (GIS) enables spatial management and visualisation of the underground data obtained from geotechnical surveys. The creation of 3D models in voxel format constitutes a pioneering and relevant line of research. This paper evaluates the main factors resulting from the integration of different topographic sources at a territorial level for the creation of surface models that efficiently adjust the geotechnical data collected, which usually lacks global height values. This task involved designing a coordinate system and a reference grid, as well as adjusting elevation data for the selected study case: the metropolitan area of Sevilla, Spain.
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Paper Nr:	38
Title:	The Contribution of Drones to the Monitoring of Rubble-Mound Breakwaters
Authors:	Maria Henriques, Rui Capitão, Conceição Fortes, Rute Lemos, Luís G. Silva, Hugo Silva and Rúben Gonçalves
Abstract:	Breakwaters are built to promote sheltered areas, for people, ships, and harbour activities. In the design of rubble-mound breakwaters, a common type of breakwater in many countries, including Portugal, it is assumed that damage may occur in certain stretches of the structures, and therefore maintenance and repair works will be quite certainly needed. To successfully carry out these interventions, in a timely and cost-effective manner, the structures must be observed and monitored systematically. This enables one to follow their structural behaviour and, through diagnosis analysis, to specify the most suitable timespan to undertake any necessary intervention. The severity of the sea on the Portuguese coasts justified the establishment, by the National Laboratory for Civil Engineering (LNEC), of a program of Systematic Observation of Maritime Works (OSOM) which, in 2018, was improved with the introduction of drones to monitor the structural present condition, evolution condition and risk condition of the structures, namely movements and falls of blocks in the armour layers. This communication presents some results of the application of OSOM+ program on breakwaters in Sines and Algarve (Faro-Olhão and Portimão) harbours, an activity that LNEC has developed for the APS – Ports of Sines and the Algarve Authority.
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Area 6 - Domain Applications

Full Papers

Paper Nr:	17
Title:	A Water-Energy-Food Nexus Approach to Assess Land Use Trade-Offs in Small Islands
Authors:	Romain Authier, Benjamin Pillot, Guillaume Guimbretière, Pablo-Corral Broto and Carmen Gervet
Abstract:	Due to their isolation, limited resources and high population density, small islands are particularly vulnerable to multi-sectoral crises. The study of the sustainability of small island social and environmental development raises among others the challenge of balanced uses of local resources, including water, food and energy. Aspects of this are currently investigated through so called models of the Water-Energy-Food (WEF) nexus. In this paper we propose a novel approach of the WEF nexus through the optimization of scenarios that make use of Geographical Information Systems (GIS) integrated with robust optimization models coined in Operations Research. Our contribution allows the identification of trade-offs between future land use potentials and thresholds by maximizing a food Self-Sufficiency Ratio (SSR) by 2035. We show a case study of our approach on Reunion island, based on real data. Our results show through different scenarii of land use dynamics, the potential of this model as a decision-support tool.
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Paper Nr:	18
Title:	Revitalizing Walkability Scores: A New Assessment Based on Accessibility
Authors:	J. Bracquart, T. Leduc, V. Tourre and M. Servières
Abstract:	Context/Purpose. With the motivation to evaluate the suitability of a urban environment for pedestrian mobility, we revisit walkability scores from the scientific literature whose one of the most representative figure is the Walk Score, which is also commercially exploited through an eponymous website. Methods. This study refines the purpose of the foundations of walkability scores as a “pedestrian level of accessibility” score and mobilizes works on accessibility to simplify the computations of the score. The parameter “importance of an amenity” gains a more generic estimation method. Lastly, the scoring is proposed for different trip categories before being aggregated into a global score. Results. We obtain a new scoring, which we apply to a small town with a simple urban layout for illustration purposes before computing scores for a larger and more diverse area. The scores allow us to identify different urban fabrics associated with different opportunities within walking distance. Conclusions. In the end, we provided walkability scores with a more scalable, explainable and readable methodology which led to improve their usefulness.
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Paper Nr:	31
Title:	Regime Analysis with Numerical Modelling of Wave Dynamics and Determination of Potential Flood Zones in Chancay Bay, Peru
Authors:	Jose Soto, Emanuel Guzman and Carmela Ramos
Abstract:	The wave dynamics in Chancay Bay is represented by the Delft3D numerical model, whose application is referred to the propagation and calibration of the waves in the WAVE module through the Copernicus ERA5 database and comparison with field measurements of the hologram of the Directorate of Hydrography and Navigation. (DIHIDRONAV) for medium and maximum regime conditions, in order to determine the Run Up of waves in areas of human development with the Van Der Meer and Stem methodology. In this sense, the topography of the ALOS PALSAR sentinel was extracted to determine the flood zones within the coast, whose representation is given by the length and height of the wave reached.
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Short Papers

Paper Nr:	32
Title:	Mapping Habitats by Integrating Multi-Source Land Use Land Cover Databases: Application to Red Fox in Urban Area
Authors:	Laurence Jolivet, Emmanuelle Robardet and Marianne Cohen
Abstract:	Habitats are key components for understanding wildlife space use. Having access to an accurate description of habitats can contribute to conservation programs and help define optimal landscape planning projects. In this study, we focus on the study case of red fox in a French urban environment. Our approach was to describe and to map habitats at a detailed spatial scale based on existing and available multi-source geographical databases. An automatic mapping process was proposed and then applied on the study site. The computed map was assessed based on a ground truth: depending on the land covers, the precision was good, between 69% and 94%. A GPS location dataset of red fox individuals were analysed with respect to the proposed map. Results showed consistent space use between the GPS locations and literature. They highlighted that separating land cover from land use is beneficial to consider the influence on red fox of both landscape features and their anthropic uses. The opportunity of the proposed automatic process is to be able to map habitats regarding the ecological functions of the landscape, in various environments and at different dates.
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Paper Nr:	48
Title:	What Will Virtual Reality Bring to the Qualification of Visual Walkability in Cities?
Authors:	Chongan Wang, Vincent Tourre, Thomas Leduc and Myriam Servières
Abstract:	Walking as a daily activity has become increasingly popular in recent years. It is not only good for people’s health, but it also helps reduce the air pollution and other nuisances associated with vehicle use. As such, we have witnessed the shift in urban design towards a pedestrian-friendly city. While walkability is a broad concept consisting of many different aspects, our work focuses on only one part of it, namely “visual walk-ability”. While standard research on visual walkability tends to conduct field experiments to collect data, we present a novel method to qualify visual walkability, namely assessment in virtual reality with omnidirectional videos. We analyze the possible questions we would encounter and propose an experiment with answers for each question.
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