Real-Time Flood Water Segmentation with Deep Neural Networks
Year: 2025
Type: conference-paper
Abstract: Nowadays, extreme floods pose severe threats to human lives and infrastructure, necessitating effective flood disaster management plans and systems. In this paper, we address the crucial need of real-time flood monitoring by leveraging computer vision models. To this end, benchmark deep neural networks are trained on the flood water segmentation task, using a novel dataset that was created by combining and annotating flood images from different sources. Our experimental evaluation of two real-world flood videos showcases the potential of computer vision in fast and accurate flood monitoring. Moreover, we investigated the use of semi-supervised training methods to enhance the flood segmentation performance by taking advantage of large unlabeled datasets. Our work emphasizes the potential of applying state-of-the-art big visual data analytics tools to mitigate the devastating impacts of floods on communities worldwide.
Authors: Gerontopoulos, Anastasios, Papaioannou, Dimitrios, Papaioannidis, Christos, Pitas, Ioannis
Institution: Aristotle University of Thessaloniki
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W6949501949
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