Detecting Floods from Cloudy Scenes: A Fusion Approach Using Sentinel-1 and Sentinel-2 Imagery (Proposals Track)

Qiuyang Chen (University of Edinburgh); Xenofon Karagiannis (Earth-i Ltd.); Simon M. Mudd (University of Edinburgh)

Paper PDF Slides PDF Recorded Talk NeurIPS 2022 Poster Topia Link Cite
Computer Vision & Remote Sensing Disaster Management and Relief Extreme Weather Time-series Analysis

Abstract

As the result of climate change, extreme flood events are becoming more frequent. To better respond to such disasters, and to test and calibrate flood models, we need accurate real-world data on flooding extent. Detection of floods from remote sensed imagery suffers from a widespread problem: clouds block flood scenes in images, leading to degraded and fragmented flood datasets. To address this challenge, we propose a workflow based on U-Net, and a dataset that detects flood in cloud-prone areas by fusing information from the Sentinel-1 and Sentinel-2 satellites. The expected result will be a reliable and detailed catalogue of flood extents and how they change through time, allowing us to better understand flooding in different morphological settings and climates.

Recorded Talk (direct link)

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