Map Floodwater Radar Imagery using Machine Learning Algorithms

Thanh-Nghi Doan; Duc-Ngoc Le-Thi

doi:10.31436/iiumej.v26i1.3157

Authors

Thanh-Nghi Doan An Giang University https://orcid.org/0000-0002-9957-0948
Duc-Ngoc Le-Thi An Giang University

DOI:

https://doi.org/10.31436/iiumej.v26i1.3157

Keywords:

Flood mapping, Fine-tunning, Radar imagery, U-Net

Abstract

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.

ABSTRAK: Banjir merupakan bencana alam yang meluas dan mahal di seluruh dunia. Penilaian yang tepat terhadap skala banjir secara hampir masa nyata adalah penting bagi kerajaan dan organisasi kemanusiaan. Maklumat ini memperkukuhkan sistem amaran awal, menilai risiko, dan membimbing usaha bantuan yang lebih berkesan. Oleh itu, pemetaan banjir yang tepat adalah penting untuk menyelamatkan nyawa melalui sistem amaran awal yang lebih baik dan respons kecemasan yang disasarkan. Dalam kajian ini, imej radar yang tersedia pada Planetary Computer Data digunakan untuk melatih model U-Net yang direka khas untuk melabelkan piksel yang terjejas oleh banjir dalam imej daripada kejadian banjir. Bagi mengenal pasti model ditala-halus yang paling cekap, blok-blok berlainan dalam arkitektur pengekod U-Net telah ditala-halus, dan hasilnya dibandingkan. Hasilnya, model dengan blok 1 dan 2 yang ditala-halus menunjukkan skor Intersection over Union (IoU) tertinggi sebanyak 78.904%, iaitu peningkatan sebanyak 8.663% berbanding kaedah asas.

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