2026-10-14T09:32:58Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34037157 2026-09-30T23:06:10Z category_146 category_21 category_272 category_734 category_931 category_811 portal_87 item_type_6 month_year_09_2026

A method for identifying urban flood inundation based on single-temporal remote sensing imagery Chenglong Yao (13719415) Guoqiang Wang (429500) Yinglan A. (25146960) Yuntao Wang (2871704) Jin Wu (216487) Baolin Xue (17999101) Qiao Wang (174321) Space Science Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Urban flood inundation single temporal remote sensing extraction from impervious surface in-orbit computing <p>Rapid mapping of urban flood inundation is critical for emergency response and decision-making. However, existing methods often rely heavily on multi-temporal data or deep learning models, limiting their timeliness and computational efficiency. This study proposes an automated method for mapping urban flood inundation from a single post-event image acquired by the Sentinel-2 Multispectral Instrument. The method adopts a three-stage framework comprising spectral-index-based segmentation, morphological-feature-based separation, and spatial-topological assessment. It was evaluated using three flood events in Zhengzhou, China; Manville, United States; and Porto Alegre, Brazil. Independent validation yielded overall accuracies of 0.9985, 0.9371, and 0.8834; precision values of 0.6719, 0.6771, and 0.8229; and F1-scores of 0.8037, 0.7001, and 0.7615, respectively. The extracted flood extents were further compared with those obtained using two conventional approaches and were generally consistent across the three study areas. Agreement F1-scores ranged from 0.694 to 0.870 relative to pre- and post-flood change detection based on the modified normalised difference water index, and from 0.603 to 0.866 relative to newly inundated area extraction using the European Commission Joint Research Centre permanent surface-water mask. On an experimental platform equipped with an NVIDIA Jetson AGX Orin, processing the Porto Alegre, Manville, and Zhengzhou scenes required 24.3, 2.68, and 19.8 s, respectively. These quantitative results indicate that the proposed method can identify major urban flood inundation areas under diverse geographical and hydrological conditions. Requiring only a single post-event multispectral image, no model training, and no high-performance computing, the method provides a lightweight solution for rapid urban flood inundation mapping and resource-constrained applications, including onboard satellite processing.</p> 2026-09-30T23:06:10Z Text Journal contribution 10.6084/m9.figshare.34037157.v1 https://figshare.com/articles/journal_contribution/A_method_for_identifying_urban_flood_inundation_based_on_single-temporal_remote_sensing_imagery/34037157 CC BY 4.0