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Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain / Yolanda Torres in International journal of applied Earth observation and geoinformation, vol 81 (September 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 81 (September 2019) . - pp 161-175 Titre : Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain Type de document : Article/Communication Auteurs : Yolanda Torres, Auteur ; José Juan Arranz, Auteur ; Jorge M. Gaspar-Escribano, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 161-175 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage automatique [Termes IGN] données lidar [Termes IGN] empreinte [Termes IGN] Espagne [Termes IGN] extraction de traits caractéristiques [Termes IGN] hauteur du bâti [Termes IGN] image multibande [Termes IGN] image satellite [Termes IGN] orthophotographie [Termes IGN] risque urbain [Termes IGN] segmentation d'image [Termes IGN] séisme [Termes IGN] stratification de données [Termes IGN] vulnérabilité [Termes IGN] zone à risque Résumé : (auteur) We present a procedure for assessing the urban exposure and seismic vulnerability that integrates LiDAR data with aerial images from the Spanish National Plan of Aerial Orthophotography (PNOA). It comprises three phases: first, we segment the satellite image to divide the study area into different urban patterns. Second, we extract building footprints and attributes that represent the type of building of each urban pattern. Finally, we assign the seismic vulnerability to each building using different machine-learning techniques: Decision trees, SVM, logistic regression and Bayesian networks. We apply the procedure to 826 buildings in the city of Lorca (SE Spain), where we count on a vulnerability database that we use as ground truth for the validation of results. The outcomes show that the machine learning techniques have similar performance, yielding vulnerability classification results with an accuracy of 77%–80% (F1-Score). The procedure is scalable and can be replicated in different areas. This is particularly relevant in Spain, where more than seven hundred towns have to develop seismic risk studies in the years to come, according to the General Direction of Civil Protection and Emergencies. It is especially interesting as a complement to conventional data gathering approaches for disaster risk applications in cities where field surveys need to be restricted to certain areas, dates or budget. Numéro de notice : A2019-471 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.05.015 Date de publication en ligne : 25/05/2019 En ligne : https://doi.org/10.1016/j.jag.2019.05.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93617 [article]