Asif Mahmud
- Co-Director, IPT Wing
Lecturer
Department of Computer Science and Engineering
Independent University, Bangladesh
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
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 mythRemote sensingComputer scienceComputer visionArtificial intelligence
CCDS Authors
Ashraful Islam, PhD
- Center Director, CCDS
- Director, HCI Wing
Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh (IUB)Research Interests
Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact
M Ashraful Amin, PhD
- Founder Director, CCDS
- Director, AI/ML, HCI & DS Wings
Research Interests
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics
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