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

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

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 defin...

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Bibliographic Details
Main Authors: Mauro Francini, Carolina Salvo, Antonio Viscomi, Alessandro Vitale
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Remote Sensing
Subjects:
automatic building extraction
remote sensing
deep learning
U-Net
semantic segmentation
natural disasters
Online Access:https://www.mdpi.com/2072-4292/14/17/4279

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