Conference Paper2024

Segment Anything Model 2 (SAM 2) for Accurate Flood Detection in Remote Sensing Imagery

Nusrat Sultana, Anika Islam, Yousra Tabassum, Jahanggir Hossain Setu, Asif Mahmud, Ashraful Islam, M. Ashraful Amin

2024 7th Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII 2024)

IEEE, pp. 1-5

  • Sustainable Cities and Communities — Relevance: 70%
  • Climate Action — Relevance: 71%
DOI: 10.1109/ceii65291.2024.00048URL

Abstract

Floods are among the most devastating natural dis-asters. It causes extensive damage to infrastructure, agriculture, and most importantly, human life. Timely and accurate flood detection is very important for minimizing these impacts. This study explores the use of Deep Learning (DL) models to enhance flood detection through image segmentation of Synthetic Aperture Radar (SAR) imagery. Three segmentation models are compared namely, U-Net, X-Net, and Segment Anything Model 2 (SAM 2), using the NASA IMPACT ETCI 2021 flood detection dataset. The results show that SAM 2 performs better than U-Net and X-Net across key metrics. SAM 2 improves accuracy by almost 1 % over U-Net and 1.64% over X-Net. It also demonstrates a higher precision rate, reducing false positives with a 1.25 % increase compared to U-Net and a 2.32% improvement over X-Net. Moreover, SAM 2 significantly enhances recall, detecting 8.71 % more flood-affected areas than both U-Net and X-Net. In terms of segmentation quality, SAM 2 achieves a higher Dice coefficient and Jaccard index, indicating more accurate flood zone identification and boundary detection, especially in smaller or complex areas, SAM 2 proves to be the most reliable model for flood detection.

Keywords

  • flood detection
  • U-Net
  • synthetic aperture radar images
  • Segment Anything Model 2
  • Image segmentation
  • flood zone mapping
  • Remote sensing

CCDS Authors

Asif Mahmud
Asif Mahmud

Asif Mahmud

  • Co-Director, IPT Wing

  • Lecturer

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, Pattern Recognition, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

References

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