GISTAM 2021 Abstracts

Area 1 - Data Acquisition and Processing
Full Papers Short Papers
Area 2 - Remote Sensing
Full Papers Short Papers
Area 3 - Modeling, Representation and Visualization
Full Papers Short Papers
Area 4 - Knowledge Extraction and Management
Full Papers Short Papers
Area 5 - Domain Applications
Short Papers

Area 1 - Data Acquisition and Processing

Full Papers
Paper Nr: 12
Title:

Deep Learning Application for Urban Change Detection from Aerial Images
Authors:

Tautvydas Fyleris, Andrius Kriščiūnas, Valentas Gružauskas and Dalia Čalnerytė
Abstract: Urban growth estimation is an essential part of urban planning in order to ensure sustainable regional development. For such purpose, analysis of remote sensing data can be used. The difficulty in analysing a time series of remote sensing data lies in ensuring that the accuracy stays stable in different periods. In this publication, aerial images were analysed for three periods, which lasted for 9 years. The main issues arose due to the different quality of images, which lead to bias between periods. Consequently, this results in difficulties in interpreting whether the urban growth actually happened, or it was identified due to the incorrect segmentation of images. To overcome this issue, datasets were generated to train the convolutional neural network (CNN) and transfer learning technique has been applied. Finally, the results obtained with the created CNN of different periods enable to implement different approaches to detect, analyse and interpret urban changes for the policymakers and investors on different levels as a map, grid, or contour map.
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Paper Nr: 40
Title:

A New Soil Degradation Method Analysis by Sentinel 2 Images Combining Spectral Indices and Statistics Analysis: Application to the Cameroonians Shores of Lake Chad and Its Hinterland
Authors:

Sébastien Gadal, Paul G. Gbetkom and Alfred N. Mfondoum
Abstract: This paper aims to model the soil degradation risk along the Cameroonian shores of Lake Chad. The processing is based on a statistical analysis of spectral indices of sentinel 2A satellite images. A total of four vegetation indices such as the Greenness Index and Disease water stress index and nine soil indices such as moisture, brightness, or organic matter content are computed and combined to characterize vegetation cover and bare soil state, respectively. All these indices are aggregated to produce one image (independent variable) and then regressed by individual indices (dependent variable) to retrieve correlation and determination coefficients. Principal Component Analysis and factorial analysis are applied to all spectral indices to summarize information, obtain factorial coordinates, and detect positive/negative correlation. The first factor contains soil information, whereas the second factor focuses on vegetation information. The final equation of the model is obtained by weighting each index with both its coefficient of determination and factorials coordinates. This result generated figures cartography of five classes of soils potentially exposed to the risk of soil degradation. Five levels of exposition risk are obtained from the "Lower" level to the "Higher": the "Lower" and "Moderate to low" levels occupy respectively 25,214.35 hectares and 130,717.19 hectares; the "Moderate" level spreads 137,404.34 hectares; the "High to moderate" and "Higher" levels correspond respectively to 152,371.91 hectares and 29,175.73 hectares.
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Paper Nr: 43
Title:

A Comparison of Interpolation Techniques in Producing a DEM from the 5 m National Geospatial Institute (NGI) Contours
Authors:

Prevlan Chetty and Solomon Tesfamichael
Abstract: Continuous elevation surfaces, which are commonly referred to as Digital Elevation Models (DEM), are vital sources of information in flood modelling. Due to the multitude of interpolation techniques available to create DEMs, there is a need to identify the best suited interpolation techniques to represent a localised hydrological environment. This study investigated the accuracies of commonly applied interpolation techniques including Inverse Distance Weighting (IDW), Nearest Neighbour (NN), Kriging, Spline and Topo to Raster interpolation techniques as applied to a 5-m interval elevation contours as a precursor to simulate a flood zone in the Roodepoort region in Johannesburg, South Africa. A 50 cm resolution DEM derived from aerial Light Detection and Ranging (LiDAR) point cloud was used as a reference to compare the five interpolations techniques. The Topo to Raster results were not significantly different from the reference data (P = 0.79 at 95% confidence level), where elevation values were on average underestimated by 0.93 m. In contrast, the spline interpolation showed the highest significant difference from the reference data (P = 0.00 at 95% confidence level), with an average underestimation of the elevation by 69.84 m. Outlier identification using standardized residual analysis flagged significant elevation outliers that were produced in the interpolation process, and it was noted that most of the outliers across all techniques coincide with areas that showed frequent topographical changes. Specifically, the largest significant differences using the Topo to Raster technique were overestimations of the elevation that occur in the upstream section of the tributary. The Spline technique in contrast showed significant underestimations of the elevation throughout the river system. Overall, the results indicate that the Topo to Raster technique is preferred to accurately represent the topography around a river system of the study area.
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Paper Nr: 46
Title:

Multi-Temporal Inundated Areas Monitoring Made Easy: The Case of Kerkini Lake in Greece
Authors:

Ioannis Manakos, Malak Kanj, Michail Sismanis, Ioannis Tsolaikidis and Chariton Kalaitzidis
Abstract: Satellite data may support management of wetland areas for monitoring of the inundation seasonality. Previously successful in Doñana and Camargue Biosphere Reserves, this study examines the transferability of unsupervised inundation mapping through automatic local thresholding in discriminating inundated areas from non-inundated ones in Kerkini Lake. Nine different alternatives of this approach are employed on Sentinel-2 (S2) Level-2A images (2016-2019). The best fit alternative was derived by the validation against local and on-site registered attributes. To overcome unfavourable atmospheric conditions, Sentinel-1 (S1) images were examined in tandem with derived S2 inundation maps (S2m), using the best fit alternative. Two S2m, one preceding and one following a target S1 image, were used to train random forest models (per pixel) to be applied to the target S1 image and derive the respective inundation map (S1m). S1m was validated against a S2m for the same date; not previously used in the training process. Classification performance reached k [0.77-0.94] and overall accuracy [88.05-97.16%] for the S2m. The evaluation of S1m showed k of 0.99 and overall accuracy between 99.71-99.88%. Automation of the process and minimum human interference supports its usage by non-specialists, e.g. for Protected Areas management.
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Short Papers
Paper Nr: 5
Title:

Using SAR Satellite Imagery for Potential Green Roof Retrofitting for Flood Mitigation in Urban Environment
Authors:

Mirka Mobilia and Antonia Longobardi
Abstract: Green roofs (GRs) represent a valid tool to mitigate the negative effects of urban floods. The aim of the present work is to test the hydrological behaviour of GRs at basin scale using Storm Water Management Model (SWMM), with an application to Preturo municipality located within the Sarno river basin (southern Italy) which during the last two decades was interested by the occurrence of many hazardous flash floods. The analysis of two sets of satellite images acquired between 1995 and 2016 by SAR sensors, showed a correlation between the increase of soil sealing and the occurrences of urban flooding during the same period. This finding suggests that the GR extensive adoption could contribute to a successful stormwater management. The suitability for GR retrofit depends on a number of criteria. In Preturo municipality, the fulfilment of these criteria was investigated using satellite images from Google Earth. The GRs retrofit potential of the studied area amounts to 7% of the total surface. The hydrological behaviour of the GR retrofit scenario was compared to the reference one which considers the actual land cover. The GR scenario better performs than the traditional one with a reduction of the runoff volume and peak flow of respectively 3.5% and 18.9% and an increase of the delay time of 8.2%.
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Paper Nr: 13
Title:

Virtual Outcrops Building in Extreme Logistic Conditions for Data Collection, Geological Mapping, and Teaching: The Santorini’s Caldera Case Study, Greece
Authors:

Fabio L. Bonali, Luca Fallati, Varvara Antoniou, Kyriaki Drymoni, Federico P. Mariotto, Noemi Corti, Alessandro Tibaldi, Agust Gudmundsson and Paraskevi Nomikou
Abstract: In the present work, we test the application of boat-camera-based photogrammetry as a tool for Virtual Outcrops (VOs) building on geological mapping and data collection. We used a 20 MPX camera run by an operator who collected pictures almost continuously, keeping the camera parallel to the ground and opposite to the target during a boat survey. Our selected target was the northern part of Santorini’s caldera wall, a structure of great geological interest. A total of 887 pictures were collected along a 5.5-km-long section along an almost vertical caldera outcrop. The survey was performed at a constant boat speed of about 4 m/s and a coastal approaching range of 35.8 to 296.5m. Using the Structure from Motion technique we: i) produced a successful and high-resolution 3D model of the studied area, ii) designed high-resolution VOs for two selected caldera sections, iii) investigated the regional geology, iv) collected qualitative and quantitative structural data along the vertical caldera cliff, and v) provided a new VO building approach in extreme logistic conditions.
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Paper Nr: 15
Title:

Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geo-spatial Technologies
Authors:

Rohan Iyer, Regina Rex, Kevin P. McPherson, Darshan Gandhi, Aryan Mahindra, Abhishek Singh and Ramesh Raskar
Abstract: There is a growing need for spatial privacy considerations in the many geo-spatial technologies that have been created as solutions for COVID-19-related issues. Although effective geo-spatial technologies have already been rolled out, most have significantly sacrificed privacy for utility. In this paper, we explore spatial k-anonymity, a privacy-preserving method that can address this unnecessary tradeoff by providing the best of both privacy and utility. After evaluating its past implications in geo-spatial use cases, we propose applications of spatial k-anonymity in the data sharing and managing of COVID-19 contact tracing technologies as well as heat maps showing a user’s travel history. We then justify our propositions by comparing spatial k-anonymity with several other spatial privacy methods, including differential privacy, geo-indistinguishability, and manual consent based redaction. Our hope is to raise awareness of the ever-growing risks associated with spatial privacy and how they can be solved with Spatial K-anonymity.
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Paper Nr: 19
Title:

An Analytical Tool for Georeferenced Sensor Data based on ELK Stack
Authors:

Thi Thu Trang Ngo, David Sarramia, Myoung-Ah Kang and François Pinet
Abstract: In the context of the French CAP 2025 I-Site project, an environmental data lake called CEBA is built at an Auvergne regional level. Its goal is to integrate data from heterogeneous sensors, provide end users tools to query and analyse georeferenced environmental data, and open data. The sensors collect different environmental measures according to their location (air and soil temperature, water quality, etc.). The measures are used by different research laboratories to analyse the environment. The main component for data shipping and storing is the ELK stack. Data are collected from sensors through Beats and streamed by Logstash to Elasticsearch. Scientists can query the data through Kibana. In this paper, we propose a data warehouse frontend to CEBA based on the ELK stack. We as well propose an additional component to the ELK stack that operates streaming ETL which allows integrating and aggregating streaming data from different sensors and sources given the user configuration in order to provide end users more analytical capabilities on the data. We show the architecture of this system, we present the functionalities of the data lake through examples, and finally, we present an example dashboard of the data on Kibana.
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Paper Nr: 28
Title:

Derivation of Wildfire Ignition Index using GIS-MCDA from High-Resolution UAV Imagery Data and Perception Analysis in Settlement Sali, Dugi Otok Island (Croatia)
Authors:

Ivan Marić, Ante Šiljeg and Fran Domazetović
Abstract: In recent years, wildfires have become one of the most hazardous natural disasters because of their overall impact on the natural and urban environment. In this paper, we have generated a wildfire risk ignition index for the Sali settlement (Dugi Otok, Croatia). This model was generated within the INTERREG PEPSEA (Protecting the Enclosed Parts of the Sea in Adriatic from pollution) project. Wildfire ignition index is based on the GIS-MCDA (Multi-Criteria Decision Analysis). The process was performed using 13 criteria grouped in five clusters. Criteria were derived from high-resolution multispectral (5 bands) orthomosaic and digital terrain model (DTM) produced from imagery acquired with Matrice 600 Pro and Matrice 210 RTK V2 UAV. The criteria weights were determined using the AHP (Analytic Hierarchy Process). The model of wildfire ignition risk was classified into five classes, from very low (1) to very high (5). The model indicates that 14.14 % of the study area falls in a very high (5) ignition risk zone. The fire-risk perception was analyzed and the wildfire ignition model was evaluated using a questionnaire. The results indicate that all recent wildfire ignition locations occurred in high (4) and very high (5) risk class. Furthermore, the population recognized wildfires as a moderate threat to the ecosystem of the wider Sali area. A set of specific management measures has been proposed to prevent wildfire ignition. This proposed methodological framework and results can provide valuable information and specific management tools to local government.
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Paper Nr: 42
Title:

Vegetation Filtering using Colour for Monitoring Applications from Photogrammetric Data
Authors:

M. A. Núñez-Andrés, Albert Prades and Felipe Buill
Abstract: Photogrammetry is one of the widest techniques used to monitor terrain changes which occur due to natural process and geological natural risk zones. In order to carry out terrain monitoring, it is necessary to eliminate all the non-ground elements. One of the most variable elements in this monitoring is the presence of vegetation, which obscures the ground and can significantly mislead any multitemporal analysis to detect terrain changes. Therefore, the focus of this paper is about how best to filter the vegetation to attain an accurate reading of the terrain. There are several methods to filter it based on colourising an excessive greenness vegetation index or non-visible channels as the IR in the well-known index NVDI. However, achieving this kind of information is not always possible because its high cost. Instead this channel we can add new information using the HSV colour space obtained from the RGB information. In this paper, we propose a double possibility, on one hand work with RGB+HSV for a supervised segmentation on images. On the other, to use excessive greenness vegetation indices and RGB+HSV for the segmentation of point clouds. The results shown that the use of additional channels HSV can significantly improve the segmentation in both studies, and therefore render a much more accurate assessment of the underlying terrain
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Area 2 - Remote Sensing

Full Papers
Paper Nr: 7
Title:

Air and Water Quality Improvement during COVID-19 Lockdown
Authors:

Pedro Silva, Mariana Ávila and Márcia Gonçalves
Abstract: COVID-19 pandemic forced many countries to adopt lockdown measures, temporarily closing factories, diminish maritime traffic and reducing the mobility of people in the cities. Analysis from the Tropospheric Monitoring Instrument (TROPOMI) and Ocean and Land Colour Instrument (OLCI) on board Europe’s Sentinel-5P, 3A/B respectively, for the first wave of the COVID-19, have shown a substantial improvement in air and water quality. More specifically, since COVID-19 lockdown until the end April, Lisbon and Porto were at their lowest PM10 levels of about 20% and a drop of 33% in 2 years, while Madrid had a significant drop since lockdown with vales significantly below 2018 levels but still close to 2019 levels. In terms of NO2 levels, Lisbon had an historical minimum of the last 2 years, dropping more than 40% during most of April 2020. Finally, Madrid had 2-year lowest level of more than 30% since lockdown. Concerning the water quality in the Portuguese coastal waters, it was verified an increase in water transparency since confinement started until May, accordingly to the Total Suspended Matter (TSM) indicator. From February to March, March to April and April to May there was a reduction in TSM levels of 17%, 37% and 53% respectively.
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Paper Nr: 11
Title:

Vertical Accuracy Assessment of ALOS PALSAR, GMTED2010, SRTM and Topodata Digital Elevation Models
Authors:

Zuleide A. Ferreira and Pedro Cabral
Abstract: Three-dimensional data of the Earth's surface can support several types of studies, such as hydrological, geomorphological, environmental monitoring, among many others. But, due to the difficulty of acquiring these data in the field, freely available Digital Elevation Models (DEM) have been widely used, and therefore, it is increasingly necessary to check their accuracy to ensure their correct applicability according to the appropriate scale. However, there are no studies which have assessed specifically the vertical accuracy of the ALOS PALSAR, GMTED2010, SRTM and Topodata DEMs according to Brazilian Cartographic Accuracy Standard (PEC). In this sense, this paper aims to evaluate the quality of the above-mentioned DEMs by using the official high accuracy altimetric network data of the Brazilian Geodetic System. Statistical analysis of errors results demonstrated that the DEMs have applications compatible with 1:100,000 scales or smaller than this, and although the GMTED2010 presented a lower accuracy than the other DEMs, it also could be classified in the same accuracy category according to the Brazilian PEC. We conclude that DEMs assessment is very important to ensure their correct application as they can be used in many researches since these data are available for practically all areas of the planet.
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Paper Nr: 22
Title:

Mapping Siberian Arctic Mountain Permafrost Landscapes by Machine Learning Multi-sensors Remote Sensing: Example of Adycha River Valley
Authors:

Moisei Zakharov, Sébastien Gadal, Yuri Danilov and Jūratė Kamičaitytė
Abstract: The landscape taxonomy has a complex structure and hierarchical classification with indicators of their recognition, which is based on a variety of heterogeneous geographic territorial and expert knowledge. This inevitably leads to difficulties in the interpretation of remote sensing data and image analysis in landscape research in the field of classification and mapping. This article examines an approach to the analysis of intra-season Landsat 8 OLI images and modeling of ASTER GDEM data for mapping of mountain permafrost landscapes of Northern Siberia at the scale of 1: 500,000 as well as its methods of classification and geographical recognition. This approach suggests implementing the recognition of terrain types and vegetation types of landscape types. The 8 types of the landscape have been identified by using the classification of the relief applying Jenness's algorithm and the assessment of the geomorphological parameters of the valley. The 6 vegetation types have been identified in mountain tundra, mountain woodlands, and valley complexes of the Adycha river valley in the Verkhoyansk mountain range. The results of mapping and the proposed method for the interpretation of remote sensing data used at regional and local levels of studying the characteristics of the permafrost distribution. The work contributes to the understanding of the landscape organization of remote mountainous permafrost areas and to the improvement of methods for mapping the permafrost landscapes for territorial development and rational environmental management.
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Paper Nr: 47
Title:

Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing
Authors:

Irini Soubry, Ioannis Manakos and Chariton Kalaitzidis
Abstract: Vegetation phenology refers to changes in seasonal patterns of vegetation cycles, such as flowering and leaf fall, influenced by annual and seasonal fluctuations of biotic and abiotic drivers. Information about phenology is crucial for unravelling the underlying biological processes across vegetation communities in space and time. It is also important for ecosystem and resources management, conservation, restoration, policy and decision-making on local, national, and global scales. Numerous approaches to register Land Surface Phenology (LSP) appeared since Earth Observation from space became possible a few decades ago. This paper attempts to capture current progress and new capacities that arose with the advent of the free data policy, the Sentinel-era, new multispectral satellite sensors, cloud computing, and machine learning in LSP for natural and semi-natural environments. Spaceborne sensors’ capacity to capture LSP, data fusion, and synergies are discussed. Information about retrieval methods through open-source tools and global LSP products and phenology networks are presented.
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Short Papers
Paper Nr: 20
Title:

Multitemporal Remote Sensing for Invasive Prosopis Juliflora Plants Mapping and Monitoring: Sharjah, UAE
Authors:

Alya AlMaazmi and Rami Al-Ruzouq
Abstract: Prosopis juliflora is one of the 'world's most invasive trees that negatively affects native species and their ecosystems. The main obstacle for controlling Prosopis juliflora pervasion is to accurately map location as well as the distribution pattern. Locating Prosopis juliflora is a strategic priority of countries to preserve the invaded local environment. Recent advances in remote sensing, geographic information system (GIS), and Machine Learning (ML) techniques provide valuable tools for producing tree distribution maps. In this research, a supervised classification method with Support Vector Machine (SVM) supported by GIS statistical analysis was developed to map Prosopis juliflora and their pattern analysis in Sharjah, one of the major cities in the United Arab. More than 5000-pixel labels taken from Landsat-7 and Landsat-8 imagery were used to train object-based Support Vector Machine to map Prosopis juliflora. The suggested algorithm resulted in 75% accuracy compared to ground truth samples. Furthermore, multi-temporal detection showed 'that's spatial clustering pattern of the trees is changing and increasing over time. The approach adopted in this study can be applied to any other location globally.
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Paper Nr: 38
Title:

Study on Ground Motions in Southwest Bulgaria based on in-Situ and Satellite Data
Authors:

Mila Atanasova-Zlatareva, Hristo Nikolov and Nikolay Dimitrov
Abstract: In the last decades data from satellites are being used more frequently to study the ground movements. This fact is evidenced by the increased number of research papers and projects using freely provided data by space agencies such ESA (European Space Agency) and JAXA (Japan Aerospace Exploration Agency) and increased revisiting time of the new instruments on-board satellites. Other reason for this increase are the latest developments in processing methods such as PSI (Persistent Scatterer Interferometry) and even increasing number of cloud processing options provided by universities and research centres. Nevertheless the information obtained by this manner has some drawbacks for example moderate spatial resolution. This is why in-situ data from precise GNSS (Global Navigation Satellite System) measurements are essential. In this study the authors used both kinds of data to study one of the regions of Bulgaria which is recognized to be highly prone to seismic and geological hazards namely the Southwest region. For this research two sources of data have been used – SAR (Synthetic Aperture Radar) data from Sentinel-1 mission of ESA and in-situ acquired contemporary and older GPS (Global Positioning System) data. As a result of SAR data processing produced were interferometric images from ascending and descending orbits to decrease the effect of the mountainous topography, while the results from the GNSS measurements were used for verification.
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Paper Nr: 39
Title:

Evaluating a Convolutional Neural Network and a Mosaic Image Database for Land Use Segmentation in the Brazilian Amazon Region
Authors:

Joel Parente de Oliveira, Marly F. Costa and Cícero C. Filho
Abstract: This study presents an image database and a convolutional neural network for the segmentation of land use in agriculture, forest and pasture classes. LANDSAT-8/OLI images from an area of the Brazilian Amazon region were used. The reference data were extracted from the results of the TerraClass project in 2014. The image database was generated in two versions: the first with six bands and the second with three bands. Each version of the data set has 4,000 images and size 400x400 pixels. Each image was generated using the mosaic technique. Each mosaic image is created from small agricultural, forest and grassland patches that are extracted from satellite images. The mosaic image is created with almost the same amount of agriculture, forest and pasture patches. The convolutional neural network architecture was evaluated together with three optimization methods: SGDM, ADAM and RMSProp and the dropout and L2 regularization for generalization improvement. The best model, CNN + optimization method + technique for generalization improvement, evaluated on the validation set, was used to segment some regions of the Amazon. The best results were obtained using the ADAM optimization method and L2 regularization. The accuracy values obtained for the evaluated images were above 94%.
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Area 3 - Modeling, Representation and Visualization

Full Papers
Paper Nr: 37
Title:

A Study of Higher Order Volume Scattering in a Layer of Discrete Random Scatterers
Authors:

Muhamad J. Jamri, Syabeela Syahali and Dina A. Wahid
Abstract: Remote sensing has been widely used as an earth observation technique to study the polar region. Volume scattering process is one of the scattering processes that occur in a layer of discrete random scatterers in remote sensing. In certain layer, volume scattering is significant and important to determine the value of backscattering coefficient. Previous study modelled the volume scattering only for first and second order. In this paper, a third order volume backscattering coefficient formulation is derived and added into the theoretical modelling of volume scattering, and its backscattering coefficient is analysed for different types of layer configuration embedded with discrete random scatterers. The condition of which the third order volume scattering may be important is studied. Results show that, third order volume scattering activity is significant when the scatterers in the layer are larger and with higher permittivity in both lower and higher incident angles, for all the frequency range studied.
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Short Papers
Paper Nr: 21
Title:

A Journey to Salamis Island (Greece) using a GIS Tailored Interactive Story Map Application
Authors:

Varvara Antoniou, Paraskevi Nomikou, Konstantinos Papaspyropoulos, Odysseas Karatzaferis, Othonas Vlasopoulos, Christos Stentoumis and Ilias Kalisperakis
Abstract: Web GIS applications have been used to communicate and showcase spatial information to the general public. In the demonstrated Web GIS application, the aim was to highlight the importance of a historic area, Salamis island (Greece), through its natural and anthropogenic environment using narrative text, multimedia, and web content as well as geospatial data and 3D visualization. Using StoryMaps, a widespread geographical visualization approach, used for science and spatial data communication, information, education, and dissemination, new functions combining many scientific fields were integrated, producing an interactive responsive web app in such a way that scientific knowledge can be received and comprehended by a broader audience.
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Paper Nr: 26
Title:

Modeling Normal and Extreme Wave Conditions in Callao Bay, Peru using Reanalysis Data
Authors:

Rafael Pimentel, Emanuel Guzman and Carmela Ramos
Abstract: Numerical simulations of wave conditions in Callao bay in normal and extreme conditions were carried out to characterize the wave dynamics in the bay. Bathymetry data from the navigation charts to represent bottom depth were used. Waves in deep waters from numerical reanalysis were calibrated with satellite data that have allowed define scenarios of wave propagation to shallow water in normal and extreme conditions. Model results were compared with in situ wave data obtaining good approximation between modeling and observed waves. Results indicates that waves coming from Southwest and South-Southwest, which is the most predominant waves in deep waters, due to the diffraction effects caused by San Lorenzo Island generate two areas with different wave height conditions, in this way in the area affected by diffraction wave reach height between 0.5 to 1m, while area unaffected by diffraction effects wave reach heigh between 2 to 5m. Waves coming from Northwest has more influence in the bay, due to diffraction effects are neglected and in general terms all the bay increase the wave height around to 2 to 5m.
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Paper Nr: 27
Title:

Integrating Population Surveys using Spatial Visual Analytics: A Case Study on Nutrition and Health Indicators of Children under Five in India
Authors:

Harshitha Ravindra and Jaya Sreevalsan-Nair
Abstract: Large-scale population surveys are beneficial in gathering information on the performance indicators of public well-being, including health and socio-economic standing. However, conducting national population surveys for low and middle-income countries (LMIC) with high population density becomes challenging. Economizing this activity, multiple surveys with different goals are decentralized and implemented by various agencies. Some of the surveys tend to overlap in outcomes with spatial/temporal or both scopes. Mining data jointly from surveys with significant overlap gives new insights while preserving their autonomy. We propose a three-step workflow for integrating surveys using spatial analytic workflow supported by visualizations. We implement the workflow on a case study using two recent population health surveys in India to study malnutrition in children under five. Our case study focuses on finding hotspots and coldspots for malnutrition, specifically undernutrition, by integrating both surveys’ outcomes. Malnutrition in children under five is a pertinent global public health problem prevalent in India. Our work shows that such an integrated analysis is beneficial along with preliminary analyses of existing national surveys to find new insights while maintaining their autonomy.
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Paper Nr: 30
Title:

Civic-Tech and Volunteered Geographic Information under the COVID-19 Pandemic: A Japanese Case Study
Authors:

Koshiro Suzuki
Abstract: In the early spring of 2020, a new infectious disease, COVID-19, emerged and spread globally, showing how vulnerable humans are to novel viral threats. Evidently, this crisis has inspired new technological and social innovations. The aim of this paper is to provide a brief overview of the application of civic tech and volunteered geographic information to confront the disease, which spontaneously emerged after the first case was confirmed in Japan in late January 2020. The trend of participatory Geographic Information Systems/PGIS that emerged from the GIS controversy in the 1990s went through crisis mapping and has demonstrated a new way of using GIS via social participation in the 21st century.
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Paper Nr: 45
Title:

An Improved Approach for Effective Describing Geometric Data in ifcOWL through WKT High Order Expressions
Authors:

Dongming Guo, Erling Onstein and Angela L. Rosa
Abstract: Building Information Models (BIM) are considered as building digital representations, including comprehensive geometric and non-geometric information. For improving BIM interoperability, the semantic related technologies have been the one of main approaches for processing BIM data. Currently, ifcOWL is a recommended Web Ontology Language (OWL) representation of the Industry Foundation Classes (IFC) schema. When BIM geometric data is translated into ifcOWL representations, the excessive number of triples will be produced, and the generated Resource Description Framework (RDF) file will also be extremely bigger than the IFC original file. For generating concise geometric representation in Semantic Web context, Well-known text (WKT) has been widely used to describe BIM geometry data in ifcOWL. However, to avoid losing semantic information, only some simple pre-existing WKT expressions (Point or LineString) are used to describe BIM geometric aggregated data in semantics context. For solving this issue, we propose an improved approach that can represent BIM geometric data in ifcOWL ontology through WKT high order expressions. This representation can not only take full advantage of pre-existing WKT expressions to generate a more concise RDF representation, but also reduce the loss of semantic information.
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Paper Nr: 32
Title:

Deploying Urban Agricultural System for an Innovative and Sustainable Urban Renewal
Authors:

Sarkissian Fanny, Loyer Teddy and Antoni Jean-Philippe
Abstract: This article claims to present the interest of a systemic method mobilisation in order to study the urban agricultural system and to characterise its sustainability. This logical reasoning is based on the principle that urban agriculture can be a lever for sustainable city, and that this effect requires a frame for planning urban agriculture projects. Hence, it presents the development prospects of a decision support system, based on an urban agricultural system, allowing prospective studies for urban agriculture deployment.
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Area 4 - Knowledge Extraction and Management

Full Papers
Paper Nr: 25
Title:

Definition of an Enriched GIS Network for Evacuation Planning
Authors:

Evans E. Howard, Lorenza Pasquini, Claudio Arbib, Antinisca Di Marco and Eliseo Clementini
Abstract: Among the most serious natural disasters, earthquakes cause severe damages to infrastructures and building, can kill or injure thousands of humans and animals and, in the luckiest circumstances, just make people homeless destroying communities, habitats, economies and mental equilibrium. In order to minimise the loss of lives, an effective evacuation plan to cope with worldwide disasters is required. In this paper we describe a novel approach to timely formulate an evacuation plan of an area struck by an earthquake. The proposed solution leverages on a two-steps modeling framework: i) a method that extracts from enriched GIS data a network description of the area to be evacuated; ii) a dynamic optimization model that calculates the safest paths citizens should follow to reach pre-identified safe areas. While the network is computed off-line at design time, the optimization model, or one of its reductions, can be embedded in a real-time system that, recomputing it several times, can guide citizen after a natural disaster even in case of high dynamic scenario. Our approach is demonstrated on a real study case: the medieval center of the Italian town of Sulmona, for which detailed GIS data with information on the urban structure and building vulnerability are available.
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Short Papers
Paper Nr: 17
Title:

Multicriteria Spatial Analysis to Map Artificial Groundwater Recharge Zones: Northern UAE
Authors:

Rami Al-Ruzouq, Abdallah Shanableh, Abdullah G. Yilmaz, Sunanda Mukherjee and Mohamad A. Khalil
Abstract: United Arab Emirates (UAE) ranks among the list of most water-stressed countries. Various sustainable water policies are suggested and adopted to tackle water scarcity issues. One of them is the implication of Artificial Groundwater Recharge (AGR) sites. AGR is a novel approach to collect freshwater in the aquifers and meet the water demands at lean periods for semi-arid countries like UAE. This research scrutinizes the primary thematic layers required for AGR zonation in the Central Northern Emirates and parts of Oman integrating with Remote Sensing (RS) and Geographic Information System (GIS). Several factors, which involve hydrological, geological, water quality measured in terms of total dissolved solids (TDS), groundwater level, euclidean distance from residential areas, were weighted using Analytical Hierarchical Process (AHP), and the weighted overlay was applied to derive the potential AGR map. The AGR map depicts the three best locations within the study area. Geology and geomorphology were the most influential factors affecting the AGR.
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Area 5 - Domain Applications

Short Papers
Paper Nr: 29
Title:

Derivation of Urban Planning Indicators (UPIs) using Worldview-3 Imagery and GEOBIA Method for Split Settlement, Croatia
Authors:

Rina Milošević, Silvija Šiljeg and Ivan Marić
Abstract: In most urban environments, loss of natural vegetation, the reduction of open spaces, and the rapid invasive transformation of the natural environment into impervious has happened. These changes can lead to a decline in life quality and in an increase of various economic, social, ecological, and infrastructural problems and risks. The complexity of the urban environment at various scales requires the application of high spatial and temporal resolution data in the process of urban planning. In this paper, specific urban planning indicators (UPIs), divided into two groups, have been derived for statistical circles (SC) of Split settlement in Croatia. Vegetation indicators (TCR - tree cover ratio, LCR - lawn cover ratio, GCR - green cover ratio) and indicators of urbanization (SCR - street cover ratio, BCR - building cover ratio, IMR - impervious surface ratio) were derived from the derived land cover model. It was generated from WorldView-3 (WV3) imagery with the GEOBIA method. A supervised machine learning technique support vector machine (SVM) was used. A significant spatial variability between UPIs at SCs was observed. The UPIs values at the studied level are the reflection of the historical spatial-functional development of the Split settlement. These type of UPIs can be used at the neighborhood level of urban planning and analysis of different issues in an urban environment.
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Paper Nr: 31
Title:

Study on Carrying Capacity of Resources in Danjiangkou Reservoir Area based on GIS Technologies
Authors:

Ke Liu, Yuhang Gan, Zhengyu Luo and Fanghong Ye
Abstract: The environmental health and green development of the water source area of Mid-Route of the South-North Water Diversion Project (SNWDP) is not only essential for ensuring that “The clear water is sent to Beijing”, but also an important prerequisite for sustainable development of society and economy in the water source area. Based on national geographic survey data, basic surveying and mapping data and statistical yearbook data, this paper used the model of relative carrying capacity of resources to estimate and analyze the carrying capacity of relative natural resources (CCRNR), carrying capacity of relative economic resources (CCRER) and carrying capacity of relative resources (CCRR) and their spatial-temporal changes in the Danjiangkou Reservoir area of the water source area of the Mid-Route of SNWDP from 2009 to 2015 by Geographic Information System (GIS) technologies. The results were shown that: (1) CCRR increased significantly compared with that in 2009. However, CCRR in the study area was still overloaded in 2015. (2) CCRNR was rich, but decreased from 2009 to 2015. (3) CCRER was in a overloading state, which increased obviously from 2009 to 2015. The improvement of CCRER was the main reason for the sustainable growth of carrying capacity in the study area. Therefore, the countermeasures including making rational use of natural resources, developing economic resources and controlling population quantity were proposed to promote the sustainable development of society, economy and ecology in the study area.
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