Explainable Deep Semantic Segmentation for Flood Inundation Mapping with Class Activation Mapping Techniques

Jacob Sanderson, Hua Mao, Naruephorn Tengtrairat, Raid Al-Nima, Wai Lok Woo

2024

Abstract

Climate change is causing escalating extreme weather events, resulting in frequent, intense flooding. Flood inundation mapping is a key tool in com-bating these flood events, by providing insight into flood-prone areas, allowing for effective resource allocation and preparation. In this study, a novel deep learning architecture for the generation of flood inundation maps is presented and compared with several state-of-the-art models across both Sentinel-1 and Sentinel-2 imagery, where it demonstrates consistently superior performance, with an Intersection Over Union (IOU) of 0.5902 with Sentinel-1, and 0.6984 with Sentinel-2 images. The importance of this versatility is underscored by visual analysis of images from each satellite under different weather conditions, demonstrating the differing strengths and limitations of each. Explainable Artificial Intelligence (XAI) is leveraged to interpret the decision-making of the model, which reveals that the proposed model not only provides the greatest accuracy but exhibits an improved ability to confidently identify the most relevant areas of an image for flood detection.

Paper Citation

in Harvard Style

Sanderson J., Mao H., Tengtrairat N., Al-Nima R. and Lok Woo W. (2024). Explainable Deep Semantic Segmentation for Flood Inundation Mapping with Class Activation Mapping Techniques. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1028-1035. DOI: 10.5220/0012432300003636

in Bibtex Style

@conference{icaart24,
author={Jacob Sanderson and Hua Mao and Naruephorn Tengtrairat and Raid Al-Nima and Wai Lok Woo},
title={Explainable Deep Semantic Segmentation for Flood Inundation Mapping with Class Activation Mapping Techniques},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1028-1035},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012432300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Explainable Deep Semantic Segmentation for Flood Inundation Mapping with Class Activation Mapping Techniques
SN - 978-989-758-680-4
AU - Sanderson J.
AU - Mao H.
AU - Tengtrairat N.
AU - Al-Nima R.
AU - Lok Woo W.
PY - 2024
SP - 1028
EP - 1035
DO - 10.5220/0012432300003636
PB - SciTePress