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Mapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data
Author(s): Ying Zhang; Sylvian Leblanc
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Paper Abstract

A 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.

Paper Details

Date Published: 2 October 2019
PDF: 7 pages
Proc. SPIE 11157, Remote Sensing Technologies and Applications in Urban Environments IV, 111570F (2 October 2019); doi: 10.1117/12.2527767
Show Author Affiliations
Ying Zhang, Natural Resources Canada (Canada)
Sylvian Leblanc, Natural Resources Canada (Canada)


Published in SPIE Proceedings Vol. 11157:
Remote Sensing Technologies and Applications in Urban Environments IV
Thilo Erbertseder; Nektarios Chrysoulakis; Ying Zhang; Frank Baier, Editor(s)

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