A fast approach for flood mapping over a large region using Sentinel-2 imagery

Özelbaş M. E., Karaca A. C., Amasyalı M. F.

REMOTE SENSING LETTERS, cilt.16, sa.9, ss.958-969, 2025 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 9
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1080/2150704x.2025.2522934
  • Dergi Adı: REMOTE SENSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, BIOSIS, CAB Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.958-969
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Floods impact millions globally, necessitating rapid mapping techniques for effective disaster management response. This study introduces a novel patch-wise classification framework and the ChangeSEN2-FL dataset, comprising 701 flood and 1216 non-flood multispectral bitemporal patch pairs from diverse global regions using Sentinel-2 imagery. Each 5⁢km×5⁢km patch includes 12 pre-event and 12 post-event bands. Using the ChangeSEN2-FL dataset, we developed a classification framework with improved generalization, utilizing multiple normalized difference indices (NDIs) for reliable flood detection. To address flood variability, we introduce a multiple thresholding approach (MTA) for NDIs within a dual-input ResNet50-based architecture. This setup utilizes false-colour (FC) and true-colour (TC) RGB composites to improve flood detection generalizability. The proposed framework is tested on a held-out set and three case studies from Madagascar, Pakistan, and Australia. The case studies test the global applicability of the model framework, comparing performance across regular RGB and dual-input configurations of single and multiple thresholded false-colour RGB. The held-out test results show a 3% improvement over TC RGB-based model, achieving 97% accuracy. Case studies further confirm adaptability, with 7.5% and 55% improvement over TC RGB-based model in Madagascar and Pakistan, respectively, demonstrating strong generalization across diverse flood scenarios.