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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 t he greatest accuracy but exhibits an improved ability to confidently identify the most relevant areas of an image for flood detection.
Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Agents and Artificial Intelligence
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
Number of pages8
ISBN (Electronic)9789897586804
Publication statusPublished - 26 Feb 2024
Event16th International Conference on Agents and Artificial Intelligence - Rome, Italy
Duration: 24 Feb 202426 Feb 2024
Conference number: 16

Publication series

ISSN (Electronic)2184-433X


Conference16th International Conference on Agents and Artificial Intelligence
Abbreviated titleICAART
Internet address

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