TY - JOUR

T1 - Automatic flood detection from sentinel-1 data using deep learning architectures

AU - Ghosh, B.

AU - Garg, S.

AU - Motagh, M.

N1 - Funding Information: The whole dataset was obtained by the authors of this study from IMPACT 2021 ETCI Competition on Flooding Detection GitHub page (https://nasa-impact.github.io/etci2021/). This work was supported by the HEIBRiDS research school (ht-tps://www.heibrids.berlin/) and partly by the Helmholtz project, AI for Near-Real Time Satellite-based Flood Response (AI4Flood), which is a joint collaboration between GFZ German Research Center for Geosciences and DLR German Aerospace Center.

PY - 2022/5/17

Y1 - 2022/5/17

N2 - Floods are the most frequent, costliest natural disasters having devastating consequences on people, infrastructure, and the ecosystem. During flood events near real-Time satellite imagery has proven to be an efficient management tool for disaster management authorities. However one of the challenges is accurate classification and segmentation of flooded water. The generalization ability of binary segmentation using threshold split-based method, is limited due to the effects of backscatter, geographical area, and time of image collection. Recent advancements in deep learning algorithms for image segmentation has demonstrated excellent potential for improving flood detection. However, there have been limited studies in this domain due to the lack of large scale labeled flood event dataset. In this paper, we present two deep learning approaches, first using a UNet and second, using a Feature Pyramid Network (FPN), both based on a backbone of EfficientNet-B7, by leveraging publicly available Sentinel-1 dataset provided jointly by NASA Interagency Implementation and Advanced Concepts Team, and IEEE GRSS Earth Science Informatics Technical Committee. The dataset covers flood events from Nebraska, North Alabama, Bangladesh, Red River North, and Florence. The performances of both networks were evaluated with multiple training, testing, and validation. During testing, the UNet model achieved the meanIOU score of 75.06% and the FPN model achieved the meanIOU score of 75.76%.

AB - Floods are the most frequent, costliest natural disasters having devastating consequences on people, infrastructure, and the ecosystem. During flood events near real-Time satellite imagery has proven to be an efficient management tool for disaster management authorities. However one of the challenges is accurate classification and segmentation of flooded water. The generalization ability of binary segmentation using threshold split-based method, is limited due to the effects of backscatter, geographical area, and time of image collection. Recent advancements in deep learning algorithms for image segmentation has demonstrated excellent potential for improving flood detection. However, there have been limited studies in this domain due to the lack of large scale labeled flood event dataset. In this paper, we present two deep learning approaches, first using a UNet and second, using a Feature Pyramid Network (FPN), both based on a backbone of EfficientNet-B7, by leveraging publicly available Sentinel-1 dataset provided jointly by NASA Interagency Implementation and Advanced Concepts Team, and IEEE GRSS Earth Science Informatics Technical Committee. The dataset covers flood events from Nebraska, North Alabama, Bangladesh, Red River North, and Florence. The performances of both networks were evaluated with multiple training, testing, and validation. During testing, the UNet model achieved the meanIOU score of 75.06% and the FPN model achieved the meanIOU score of 75.76%.

KW - Deep learning

KW - Feature Pyramid Network (FPN)

KW - Flood detection

KW - NASA

KW - Synthetic aperture radar

KW - Transfer learning

KW - UNet

UR - http://www.scopus.com/inward/record.url?scp=85132008444&partnerID=8YFLogxK

U2 - 10.5194/isprs-Annals-V-3-2022-201-2022

DO - 10.5194/isprs-Annals-V-3-2022-201-2022

M3 - Conference article

AN - SCOPUS:85132008444

VL - 5

SP - 201

EP - 208

JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 2194-9042

IS - 3

T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III

Y2 - 6 June 2022 through 11 June 2022

ER -