ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-1-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 113–120, 2021
https://doi.org/10.5194/isprs-annals-V-1-2021-113-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 113–120, 2021
https://doi.org/10.5194/isprs-annals-V-1-2021-113-2021

  17 Jun 2021

17 Jun 2021

DEEP LEARNING FOR AUTOMATIC BUILDING DAMAGE ASSESSMENT: APPLICATION IN POST-DISASTER SCENARIOS USING UAV DATA

A. Calantropio1, F. Chiabrando1, M. Codastefano2, and E. Bourke2 A. Calantropio et al.
  • 1Laboratory of Geomatics for Cultural Heritage (LabG4CH), Department of Architecture and Design (DAD), Polytechnic University of Turin, Viale Pier Andrea Mattioli, 39, 10125 Torino (TO), Italy
  • 2Unmanned Aircraft Systems Team (UAS), IT Preparedness and Emergency Response Branch (TECF), Technology Division (TEC), World Food Programme (WFP), Via Cesare Giulio Viola, 68, 00148 Roma, Italy

Keywords: Emergency response, Building damage assessment, deep learning, UAV photogrammetry, automatic segmentation, automatic classification

Abstract. During the last few years, the technical and scientific advances in the Geomatics research field have led to the validation of new mapping and surveying strategies, without neglecting already consolidated practices. The use of remote sensing data for damage assessment in post-disaster scenarios underlined, in several contexts and situations, the importance of the Geomatics applied techniques for disaster management operations, and nowadays their reliability and suitability in environmental emergencies is globally recognized. In this paper, the authors present their experiences in the framework of the 2016 earthquake in Central Italy and the 2019 Cyclone Idai in Mozambique. Thanks to the use of image-based survey techniques as the main acquisition methods (UAV photogrammetry), damage assessment analysis has been carried out to assess and map the damages that occurred in Pescara del Tronto village, using DEEP (Digital Engine for Emergency Photo-analysis) a deep learning tool for automatic building footprint segmentation and building damage classification, functional to the rapid production of cartography to be used in emergency response operations. The performed analyses have been presented, and the strengths and weaknesses of the employed methods and techniques have been outlined. In conclusion and based on the authors' experience, some operational suggestions and best practices are provided and future research perspectives within the same research topic are introduced.