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TitleMapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data
 
AuthorZhang, Y; Leblanc, SORCID logo
SourceRemote Sensing Technologies and Applications in Urban Environments IV, proceedings; by Erbertseder, T (ed.); Chrysoulakis, N (ed.); ZHang, Y (ed.); Baier, F (ed.); Proceedings of SPIE, the International Society of Optical Engineering vol. 11157, 111570F, 2019., https://doi.org/10.1117/12.2527767
Image
Year2019
Alt SeriesNatural Resources Canada, Contribution Series 20200476
PublisherSociety of Photo-Optical Instrumentation Engineers (SPIE)
MeetingRemote Sensing Technologies and Applications in Urban Environments IV, Society of Photo-Optical Instrumentation Engineers (SPIE); Strasbourg; FR; September 9-12, 2019
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
ProgramThematic Mapping
Released2019 10 02
AbstractA practical processing framework for EO-based detection of building damage in dense urban areas is proposed based on pre- and post-event shadow differencing. The basic data set used for the detection of damaged buildings includes LiDAR and multispectral images with high spatial resolution. The typical building damage types after a major earthquake, such as height-reduced, overturn collapse and inclination, have been considered in this study. Through a scenario case study based on simulations of both building damage and shadow, understandings of the relationship between shadow and building damage are improved for real-time response practices.
Summary(Plain Language Summary, not published)
After a major earthquake in a dense urban area, the spatial distribution of heavily damaged and collapsed buildings is indicative of the impact of the event on public safety. Timely synoptic assessment of the locations of severely damaged buildings and their damage morphologies is critical for search and rescue actions. This paper presents a novel processing methodology using remote sensing technologies and spatial modeling for detection of building damages after disaster.
GEOSCAN ID327360

 
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