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Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images / Ziyao Xing in Sustainable Cities and Society, vol 92 (May 2023)
[article]
Titre : Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images Type de document : Article/Communication Auteurs : Ziyao Xing, Auteur ; Shuai Yang, Auteur ; Xuli Zan, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104467 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] bâtiment
[Termes IGN] Chine
[Termes IGN] gestion des risques
[Termes IGN] image Streetview
[Termes IGN] inondation
[Termes IGN] milieu urbain
[Termes IGN] planification urbaine
[Termes IGN] Quickbird
[Termes IGN] segmentation sémantique
[Termes IGN] vulnérabilitéRésumé : (auteur) Urban flood risk management requires an extensive investigation of the vulnerability characteristics of buildings. Large-scale field surveys usually cost a lot of time and money, while satellite remote sensing and street view images can provide information on the tops and facades of buildings respectively. Thereupon, this paper develops a building vulnerability assessment framework using remote sensing and street view features. Specifically, a UNet-based semantic segmentation model, FSA-UNet (Fusion-Self-Attention-UNet) is proposed to integrate remote sensing and street view features and the vulnerability information contained in the images is fully exploited. And the building vulnerability index is generated to provide the spatial distribution characteristics of urban building vulnerability. The experiment shows that the mIoU of the proposed model can reach 82% for building vulnerability classification in Hefei, China, which is more accurate than the traditional semantic segmentation models. The results indicate that the integration of street view and remote sensing image features can improve the ability of building vulnerability assessment, and the model proposed in this study can better capture the correlation features of multi-angle images through the self-attention mechanism and combines hierarchy features and edge information to improve the classification effect. This study can support for disaster management and urban planning. Numéro de notice : A2023-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2023.104467 Date de publication en ligne : 23/02/2023 En ligne : https://doi.org/10.1016/j.scs.2023.104467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102826
in Sustainable Cities and Society > vol 92 (May 2023) . - n° 104467[article]La cartographie du relief : Une gageure technique et des solutions / Laurent Polidori in Géomètre, n° 2212 (avril 2023)
[article]
Titre : La cartographie du relief : Une gageure technique et des solutions Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2023 Article en page(s) : pp 38 - 48 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] canopée
[Termes IGN] crue
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inondation
[Termes IGN] modèle numérique de terrain
[Termes IGN] nuage
[Termes IGN] photogrammétrie aérienne
[Termes IGN] photogrammétrie terrestre
[Termes IGN] précision des données
[Termes IGN] qualité du modèle
[Termes IGN] représentation du relief
[Termes IGN] semis de points
[Termes IGN] télémétrie laserRésumé : (Editeur) La reconstruction d’éléments tridimensionnels a fait l’objet de nombreux développements, avec des applications dans des domaines aussi variés que l’architecture, la géologie et l’anatomie, mais c’est au relief terrestre que l’on s’inté?resse dans ce dossier. Ainsi, une grande variété de techniques de mesure (photogrammétrie, radar, lidar), mises en œuvre depuis des satellites, des avions, des drones ou à même le sol, adaptées aux différentes échelles et aux différents paysages, permettent de cartographier le relief terrestre sous la forme de nuages de points. Ceux-ci servent à construire des modèles numériques de terrain (sol) ou de surface (canopée forestière, toits), utilisés dans de nombreux domaines, pourvus qu’ils respectent des exigences de qualité comme la précision des altitudes ou la cohérence de l’hydrographie. L’évolution des instruments d’observation et des algorithmes de traitement étend les possibilités de production de modèles de relief et leur usage pour la gestion des territoires. Dans ce contexte, la formation technique constitue le nouvel enjeu pour améliorer le dialogue entre les producteurs et les consommateurs. Numéro de notice : A2023-174 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/04/2023 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102977
in Géomètre > n° 2212 (avril 2023) . - pp 38 - 48[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2023041 RAB Revue Centre de documentation En réserve L003 Disponible A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level / Laxmi Gupta in Journal of maps, vol 18 n° 2 (February 2023)
[article]
Titre : A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level Type de document : Article/Communication Auteurs : Laxmi Gupta, Auteur ; Jagabandhu Dixit, Auteur Année de publication : 2023 Article en page(s) : 33 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse multicritère
[Termes IGN] cartographie des risques
[Termes IGN] eau de surface
[Termes IGN] Inde
[Termes IGN] inondation
[Termes IGN] planification
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] ruissellement
[Termes IGN] système d'information géographique
[Termes IGN] vulnérabilitéRésumé : (auteur) Floods are frequently occurring events in the Assam region due to the presence of the Brahmaputra River and the heavy monsoon period. An efficient and reliable methodology is utilized to prepare a GIS-based flood risk map for the Assam region, India. At the regional and administrative level, the flood hazard index (FHI), flood vulnerability index (FVI), and flood risk index (FRI) are developed using multi-criteria decision analysis (MCDA) – analytical hierarchy process (AHP). The selected indicators define the topographical, geological, meteorological, drainage characteristics, land use land cover, and demographical features of Assam. The results show that more than 70%, 57.37%, and 50% of the total area lie in moderate to very high FHI, FVI, and FRI classes, respectively. The proposed methodology can be applied to identify high flood risk zones and to carry out effective flood risk management and mitigation strategies in vulnerable areas. Numéro de notice : A2023-054 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2060329 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2060329 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102387
in Journal of maps > vol 18 n° 2 (February 2023) . - 33 p.[article]Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])
[article]
Titre : Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling Type de document : Article/Communication Auteurs : Saeid Janizadeh, Auteur Année de publication : 2022 Article en page(s) : pp 8273 - 8292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] ArcGIS
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie des risques
[Termes IGN] classification par arbre de décision
[Termes IGN] colinéarité
[Termes IGN] estimation bayesienne
[Termes IGN] Extreme Gradient Machine
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] TéhéranRésumé : (auteur) The purpose of this investigation is to develop an optimal model to flood susceptibility mapping in the Kan watershed, Tehran, Iran. Therefore, in this study, three Bayesian optimization hyper-parameter algorithms including Upper confidence bound (UCB), Probability of improvement (PI) and Expected improvement (EI) in order to Extreme Gradient Boosting (XGB) machine learning model optimization and Extreme randomize tree (ERT) model for modeling flood hazard were used. In order to perform flood susceptibility mapping, 118 historic flood locations were identified and analyzed using 17 geo-environmental explanatory variables to predict flooding susceptibility. Flood locations data were divided into 70% for training and 30% for testing of models developed. The receiver operating characteristic (ROC) curve parameters were used to evaluate the performance of the models. The evaluation results based on the criterion area under curve (AUC) in the testing stage showed that the ERT and XGB models have efficiencies of 91.37% and 91.95%, respectively. The evaluation of the efficiency of Bayesian hyperparameters optimization methods on the XGB model also showed that these methods increase the efficiency of the XGB model, so that the model efficiency using these methods EI-XGB, POI-XGB and UCB-XGB based on the AUC in the testing stage were 95.89%, 96.87% and 96.38%, respectively. The results of the relative importance of the five models shows that the variables of elevation and distance from the river are the significant compared to other variables in predicting flood hazard in the Kan watershed. Numéro de notice : A2022-931 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1996641 Date de publication en ligne : 29/10/2021 En ligne : https://doi.org/10.1080/10106049.2021.1996641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102666
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 8273 - 8292[article]Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)
[article]
Titre : Modelling evacuation preparation time prior to floods: A machine learning approach Type de document : Article/Communication Auteurs : R. Sreejith, Auteur ; K.R. Sinimole, Auteur Année de publication : 2022 Article en page(s) : n° 104257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] chronométrie
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion de crise
[Termes IGN] inondation
[Termes IGN] Kerala (Inde ; état)
[Termes IGN] modèle de simulation
[Termes IGN] plan de prévention des risques
[Termes IGN] questionnaire
[Termes IGN] risque naturel
[Termes IGN] secours d'urgenceRésumé : (auteur) Flooding is a significant hazard responsible for substantial damage and risks to human life worldwide. Effective emergency evacuation to a safer location remains a concern even though the crisis can be predicted and warnings were given. During a calamity, most residents cannot quickly and securely flee. As it is crucial to start evacuation at the right time to have a safe evacuation, this study focuses on a machine learning-based model for predicting a household's evacuation preparation time in the incident of a flood. The study is based on the data collected from flood-affected people from Kerala, India, through a questionnaire. The study indicates that people's demographic, geographical and behavioural aspects, awareness of natural hazards and management are the critical components for improved emergency actions. Further, the article also analysed the characteristics of the respondents and successfully created clusters in which the respondents broadly belong, which will help the rescue team operationalize the evacuation process. Numéro de notice : A2022-819 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104257 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101986
in Sustainable Cities and Society > vol 87 (December 2022) . - n° 104257[article]Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkSpatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)PermalinkDevelopment of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])PermalinkExploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)PermalinkFlood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)PermalinkTowards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping / Sandro Martinis in Remote sensing of environment, vol 278 (September 2022)PermalinkUAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment / Katerina Trepekli in Natural Hazards, vol 113 n° 1 (August 2022)PermalinkART-RISK 3.0, a fuzzy-based platform that combine GIS and expert assessments for conservation strategies in cultural heritage / M. Moreno in Journal of Cultural Heritage, vol 55 (May - June 2022)PermalinkAssessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data / Cheng-Chun Lee in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkDetermination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkFlood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco / Brahim Benzougagh in Iranian Journal of Science and Technology - Transactions of Civil Engineering, vol 46 n° 2 (April 2022)PermalinkFlood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (March 2022)PermalinkAssessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India / Rajib Mitra in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkCIME: Context-aware geolocation of emergency-related posts / Gabriele Scalia in Geoinformatica, vol 26 n° 1 (January 2022)PermalinkPermalinkEvaluation de méthodes automatisées de cartographie des zones inondables adaptées à la prévision des crues soudaines / Nabil Hocini (2022)PermalinkFlood susceptibility mapping using meta-heuristic algorithms / Alireza Arabameri in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkHarmonisation de la production cartographique dans le cadre des Programmes d’Actions de Prévention des Inondations / Nils Deslandes (2022)PermalinkPermalinkSimulation of the meltwater under different climate change scenarios in a poorly gauged snow and glacier-fed Chitral River catchment (Hindukush region) / Huma Hayat in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkTowards urban flood susceptibility mapping using data-driven models in Berlin, Germany / Omar Seleem in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkIncorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment / Ali Azareh in Geocarto international, vol 36 n° 20 ([01/12/2021])PermalinkPotential flood hazard zone mapping based on geomorphologic considerations and fuzzy analytical hierarchy model in a data scarce West African basin / Olabanji Aladejana in Geocarto international, vol 36 n° 19 ([01/11/2021])PermalinkDisaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkFlood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model / Gaurav Talukdar in Natural Hazards, vol 109 n° 1 (October 2021)PermalinkMise en place d'un dispositif expérimental numérique pour l'enseignement des risques naturels avec le jeu vidéo Minetest / Jérôme Staub in Cartes & Géomatique, n° 245-246 (septembre - décembre 2021)PermalinkRapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkFlood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)PermalinkSpatio-temporal-spectral observation model for urban remote sensing / Zhenfeng Shao in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkA framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)PermalinkFlood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec / Jean-Marie Zokagoa in Natural Hazards, vol 107 n° 1 (May 2021)PermalinkUrban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkAn improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards / Geraldo Moura Ramos Filho in Natural Hazards, Vol 105 n° 3 (February 2021)PermalinkA dynamic bidirectional coupled surface flow model for flood inundation simulation / Chunbo Jiang in Natural Hazards and Earth System Sciences, Vol 21 n° 2 (February 2021)PermalinkOptimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)PermalinkFlood mapping from radar remote sensing using automated image classification techniques / Lisa Landuyt (2021)PermalinkGeospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data / C.M. Bhatt in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)PermalinkPermalinkPermalinkPermalinkPermalinkLarge-scale stochastic flood hazard analysis applied to the Po River / A. Curran in Natural Hazards, vol 104 n° 3 (December 2020)PermalinkChallenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco / El Mahdi El Khalk in Natural Hazards and Earth System Sciences, vol 20 n° 10 (October 2020)PermalinkUrban flooding in Britain: an approach to comparing ancient and contemporary flood exposure / T.E. O'Shea in Natural Hazards, Vol 104 n° 1 (October 2020)PermalinkArctic tsunamis threaten coastal landscapes and communities – survey of Karrat Isfjord 2017 tsunami effects in Nuugaatsiaq, western Greenland / Mateusz C. Strzelecki in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)PermalinkWater level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkAssessment of USGS DEMs for modelling pothole inundation in the prairie pothole region of Iowa / Priyadarshi Upadhyay in Geocarto international, vol 35 n° 9 ([01/07/2020])PermalinkGIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia / Dhekra Souissi in Geocarto international, vol 35 n° 9 ([01/07/2020])PermalinkRegionalization of flood magnitudes using the ecological attributes of watersheds / Bahman Jabbarian Amiri in Geocarto international, vol 35 n° 9 ([01/07/2020])PermalinkEvaluating the impact of visualization of risk upon emergency route-planning / Lisa Cheong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)PermalinkReal-time mapping of natural disasters using citizen update streams / Iranga Subasinghe in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)PermalinkDétermination conjointe des inondations et du type d’eau au moyen de l’imagerie multi-spectrale / Sabrine Amzil (2020)PermalinkPermalinkA thematic mapping method to assess and analyze potential urban hazards and risks caused by flooding / Mohammad Khalid Hossain in Computers, Environment and Urban Systems, vol 79 (January 2020)PermalinkPermalinkLes eaux de pluie maîtrisées ou en excès / Pierre Clergeot in Géomètre, n° 2173 (octobre 2019)PermalinkSpatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model / Mariana Madruga de bruto in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)PermalinkMonitoring of extreme land hydrology events in central Poland using GRACE, land surface models and absolute gravity data / Joanna Kuczynska-Siehien in Journal of applied geodesy, vol 13 n° 3 (July 2019)PermalinkClimate variability and climate change impacts on land surface, hydrological processes and water management / Yongqiang Zhang (2019)PermalinkPermalinkExploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau / Filsa Bioresita (2019)PermalinkFlash flood risk assessment in urban arid environment: case study of Taibah and Islamic universities’ campuses, Medina, Kingdom of Saudi Arabia / Mohamed Abdulrazzak in Geomatics, Natural Hazards and Risk, vol 10 n° 1 (2019)PermalinkPermalinkPermalinkPermalinkRéorganisation du SIG et valorisation des données du Parc Naturel Régional du Gâtinais français / Paul Roux (2019)PermalinkThe impact of rainfall distribution patterns on hydrological and hydraulic response in arid regions: case study Medina, Saudi Arabia / Mohamed Abdulrazzak in Arabian Journal of Geosciences, vol 11 n° 21 (November 2018)PermalinkA multi‐objective framework for analysis of road network vulnerability for relief facility location during flood hazards : A case study of relief location analysis in Bankura District, India / Omprakash Chakraborty in Transactions in GIS, vol 22 n° 5 (October 2018)PermalinkAlgorithm of land cover spatial data processing for the local flood risk mapping / Monika Siejka in Survey review, vol 50 n° 362 (August 2018)PermalinkModeling of inland flood vulnerability zones through remote sensing and GIS techniques in the highland region of Papua New Guinea / Porejane Harley in Applied geomatics, vol 10 n° 2 (June 2018)PermalinkTAGGS : grouping tweets to improve global geoparsing for disaster response / Jens A. de Bruijn in Journal of Geovisualization and Spatial Analysis, vol 2 n° 1 (June 2018)PermalinkExploring the sensitivity of coastal inundation modelling to DEM vertical error / Harry West in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)PermalinkAn open source framework for publishing flood inundation extent libraries in a Web GIS environment using open source technologies / Vinod Kumar Sharma in International journal of cartography, vol 4 n° 1 (March 2018)PermalinkEvaluation of web maps for the communication of flood risks to the public in Europe / Maaike Van Kerkvoorde in International journal of cartography, vol 4 n° 1 (March 2018)PermalinkExtraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos / Yu Feng in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)PermalinkCaractérisation et qualification de Modèles Numériques de Surfaces (MNS) - Analyse de la cohérence avec des masques d’eau / Guillaume Sutter (2018)PermalinkRegard pluridisciplinaire sur les usages sociaux de géovisualisations 3D pour la sensibilisation au risque d’inondation : Un exemple rhodanien / Julia Bonaccorsi in Revue internationale de géomatique, vol 28 n° 1 (janvier - mars 2018)PermalinkPermalinkPermalinkChangement climatique et risque inondation / William Halbecq in Géomatique expert, n° 119 (novembre - décembre 2017)PermalinkA cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 2017)PermalinkGéovisualisation de récits d’inondations et des effets dominos : De la méthode de géovisualisation à son évaluation dans un contexte expert / Cécile Saint-Marc in Revue internationale de géomatique, vol 27 n° 4 (octobre - décembre 2017)PermalinkEnseignements du test utilisateur d'une géovisualisation dynamique : des améliorations possibles pour les expériences en cartographie / Cécile Saint-Marc in Cartes & Géomatique, n° 233 (septembre - novembre 2017)PermalinkOptimization of simulation and visualization analysis of dam-failure flood disaster for diverse computing systems / Mingwei Liu in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)PermalinkHERA: A dynamic web application for visualizing community exposure to flood hazards based on storm and sea level rise scenarios / Jeanne M. Jones in Computers & geosciences, vol 109 (December 2017)PermalinkMapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest / Iurii Shendryk in Remote sensing of environment, vol 187 (15 December 2016)PermalinkDEM Fusion of elevation REST API data in support of rapid flood modelling / Heather McGrath in Geomatica, vol 70 n° 4 (December 2016)PermalinkAssessing the ecosystem service flood protection of a riparian forest by applying a cascade approach / Nina-Christin Barth in Ecosystem Services, vol 21 Part A (October 2016)PermalinkLocal-scale flood mapping on vegetated floodplains from radiometrically calibrated airborne LiDAR data / Radosław Malinowski in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkMethodology and software solutions for multicriteria evaluation of floodplain retention suitability / Rico Vogel in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)PermalinkDiagnostic study of a high‐precipitation event in the Western Mediterranean: adequacy of current operational networks / Samiro Khodayar in Quarterly Journal of the Royal Meteorological Society, vol 142 n° S1 (August 2016)PermalinkApport de la sûreté de fonctionnement à l’analyse spatialisée du risque inondation / Michaël Gonzva in Revue internationale de géomatique, vol 26 n° 3 (juillet - septembre 2016)PermalinkMapping and characterization of hydrological dynamics in coastal marsh using high temporal resolution Sentinel-1 images / Cécile Cazals in Remote sensing, vol 8 n° 7 (July 2016)PermalinkL'essor soudain du "vivre avec" le risque / Pierre Clergeot in Géomètre, n° 2136 (mai 2016)PermalinkSauver les cartes et les comprendre / Pierre Clergeot in Géomètre, n° 2136 (mai 2016)Permalink