Mapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data

Ying Zhang; Sylvian Leblanc

doi:10.1117/12.2527767

2 October 2019 Mapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data

Ying Zhang, Sylvian Leblanc

Author Affiliations +

Proceedings Volume 11157, Remote Sensing Technologies and Applications in Urban Environments IV; 111570F (2019) https://doi.org/10.1117/12.2527767
Event: SPIE Remote Sensing, 2019, Strasbourg, France

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.

Conference Presentation

Citation Download Citation

Ying Zhang and Sylvian Leblanc "Mapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data", Proc. SPIE 11157, Remote Sensing Technologies and Applications in Urban Environments IV, 111570F (2 October 2019); https://doi.org/10.1117/12.2527767

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