Abstract
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’s 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.