Authors:
Jacob Sanderson
1
;
Hua Mao
1
;
Naruephorn Tengtrairat
2
;
Raid Al-Nima
3
and
Wai Lok Woo
1
Affiliations:
1
Department of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne, U.K.
;
2
School of Software Engineering, Payap University, Chiang Mai, Thailand
;
3
Technical Engineering College, Northern Technical University, Mosul, Iraq
Keyword(s):
Deep Learning, Semantic Segmentation, Explainable Artificial Intelligence, Flood Inundation Mapping, Satellite Imagery.
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 t
he greatest accuracy but exhibits an improved ability to confidently identify the most relevant areas of an image for flood detection.
(More)