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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)  

Public [article]  inSustainable Cities and Society > vol 92 (May 2023) . - n° 104467 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] vulnérabilité Résumé : (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 [article]

La cartographie du relief : Une gageure technique et des solutions / Laurent Polidori in Géomètre, n° 2212 (avril 2023)

Public [article]  inGéomètre > n° 2212 (avril 2023) . - pp 38 - 48 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 laser Résumé : (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 [article]

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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)  

Public [article]  inJournal of maps > vol 18 n° 2 (February 2023) . - 33 p. 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] vulnérabilité Résumé : (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 [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])  

Public [article]  inGeocarto international > vol 37 n° 25 [01/12/2022] . - pp 8273 - 8292 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] Téhéran Résumé : (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 [article]

Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)  

Public [article]  inSustainable Cities and Society > vol 87 (December 2022) . - n° 104257 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'urgence Résumé : (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 [article]

Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])  

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Spatio-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)  

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Development 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])  

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Exploring 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)  

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Flood 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)  

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Towards 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)  

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UAV-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)  

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ART-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)  

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Assessing 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)  

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Determination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)  

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