| Title | Mapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data |
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| Author | Zhang, Y; Leblanc, S |
| Source | Remote 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 |
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| Year | 2019 |
| Alt Series | Natural Resources Canada, Contribution Series 20200476 |
| Publisher | Society of Photo-Optical Instrumentation Engineers (SPIE) |
| Meeting | Remote Sensing Technologies and Applications in Urban Environments IV, Society of Photo-Optical Instrumentation Engineers (SPIE); Strasbourg; FR; September 9-12, 2019 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Program | Thematic Mapping |
| Released | 2019 10 02 |
| 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. |
| 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 ID | 327360 |
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