{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:09:12Z","timestamp":1770768552742,"version":"3.50.0"},"reference-count":45,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T00:00:00Z","timestamp":1661817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Civil Engineering of University of Calabria"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Natural disasters have a significant impact on urban areas, resulting in loss of lives and urban services. Using satellite and aerial imagery, the rapid and automatic assessment of at-risk located buildings from can improve the overall disaster management system of urban areas. To do this, the definition, and the implementation of models with strong generalization, is very important. Starting from these assumptions, the authors proposed a deep learning approach based on the U-Net model to map buildings that fall into mapped landslide risk areas. The U-Net model is trained and validated using the Dubai\u2019s Satellite Imagery Dataset. The transferability of the model results are tested in three different urban areas within Calabria Region, Southern Italy, using natural color orthoimages and multi-source GIS data. The results show that the proposed methodology can detect and predict buildings that fall into landslide risk zones, with an appreciable transferability capability. During the prevention phase of emergency planning, this tool can support decision-makers and planners with the rapid identification of buildings located within risk areas, and during the post event phase, by assessing urban system conditions after a hazard occurs.<\/jats:p>","DOI":"10.3390\/rs14174279","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:13:56Z","timestamp":1661904836000},"page":"4279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Deep Learning-Based Method for the Semi-Automatic Identification of Built-Up Areas within Risk Zones Using Aerial Imagery and Multi-Source GIS Data: An Application for Landslide Risk"],"prefix":"10.3390","volume":"14","author":[{"given":"Mauro","family":"Francini","sequence":"first","affiliation":[{"name":"Laboratory of Interventions Management in Environmental Emergencies Conditions, University of Calabria, Via Pietro Bucci, Cubo 45\/B, Arcavacata di Rende, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3670-490X","authenticated-orcid":false,"given":"Carolina","family":"Salvo","sequence":"additional","affiliation":[{"name":"Laboratory of Interventions Management in Environmental Emergencies Conditions, University of Calabria, Via Pietro Bucci, Cubo 45\/B, Arcavacata di Rende, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2792-1101","authenticated-orcid":false,"given":"Antonio","family":"Viscomi","sequence":"additional","affiliation":[{"name":"Laboratory of Interventions Management in Environmental Emergencies Conditions, University of Calabria, Via Pietro Bucci, Cubo 45\/B, Arcavacata di Rende, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7163-8114","authenticated-orcid":false,"given":"Alessandro","family":"Vitale","sequence":"additional","affiliation":[{"name":"Laboratory of Interventions Management in Environmental Emergencies Conditions, University of Calabria, Via Pietro Bucci, Cubo 45\/B, Arcavacata di Rende, 87036 Rende, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neucom.2020.02.139","article-title":"Deep learning-based aerial image segmentation with open data for disaster impact assessment","volume":"439","author":"Gupta","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, W., He, C., Fang, J., Zheng, J., Fu, H., and Yu, L. 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