Map Floodwater Radar Imagery using Machine Learning Algorithms - Thanh-Nghi Doan; Duc-Ngoc Le-Thi (International Islamic University Malaysia Engineering Journal)
Flooding is a widespread and costly natural disaster around the world. Accurately assessing the extent of flooding in near real-time is crucial for governments and humanitarian organizations. This information strengthens early warning systems, evaluates risks, and guides effective relief efforts. Therefore, precise flood mapping is essential for saving lives through improved early warning systems and targeted emergency responses. In this study, radar imagery available on the Planetary Computer Data was utilized to train a U-Net model specifically designed to label flood-affected pixels in an image from a flood event. Different blocks of the U-Net encoder architecture were fine-tuned to identify the most efficient fine-tuned model, and their results were compared. As a result, the model with blocks 1 and 2 being fine-tuned demonstrated the highest Intersection over Union (IoU) score of 78.904%, an increase of 8.663% over the baseline methods.