| Title | Automated extraction of visible floodwater in dense urban areas from RGB aerial photos |
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| Author | Zhang, Y; Crawford, P |
| Source | Remote Sensing vol. 12, issue 14, 2198, 2020 p. 1-14, https://doi.org/10.3390/rs12142198  Open Access |
| Image |  |
| Year | 2020 |
| Alt Series | Natural Resources Canada, Contribution Series 20200327 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf; html |
| Province | Alberta |
| NTS | 82I/13; 82J/16; 82O/01; 82P/04 |
| Area | Calgary |
| Lat/Long WENS | -114.5000 -113.5000 51.2500 50.7500 |
| Subjects | hydrogeology; geophysics; Science and Technology; Nature and Environment; Health and Safety; floods; remote sensing; photogrammetric techniques; airphoto interpretation; urban geology; mapping
techniques; 2013 Calgary Flood; Automation; Emergency services; Methodology |
| Illustrations | location maps; aerial photographs; tables; plots; bar graphs; flow diagrams; 3-D diagrams; geoscientific sketch maps |
| Released | 2020 07 09 |
| Abstract | Rapid response mapping of floodwater extents in urbanized areas, while essential for early damage assessment and rescue operations, also presents significant image interpretation challenges. Images from
visible band (red-green-blue (RGB)) remote sensors are the most common and cost-effective for real-time applications. Based on an understanding of the differing characteristics of turbid floodwater and urban land surface classes, a robust method was
developed and automatized to extract visible floodwater using RGB band digital numbers. The methodology was applied to delineate visible floodwater distribution from very high-resolution aerial image data acquired during the 2013 Calgary flood event.
The methodology development involved segment- and pixel-based feature analysis, rule development, automated feature extraction, and result validation processing. The accuracies for the visible floodwater class were above 0.8394% and the overall
accuracies were above 0.9668% at both pixel and segment levels for three test sites with diverse urban landscapes. |
| GEOSCAN ID | 327012 |
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