AquaCluster: Using Satellite Images And Self-supervised Machine Learning Networks To Detect Water Hidden Under Vegetation

2025/06/09 by Ioannis Iakovidis, Zahra Kalantari, Iakovidis, Ioannis +7
Engineering · Environmental Science · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Flood Risk Assessment and Management #Remote-Sensing Image Classification

paper · pdf · doi:10.48550/arxiv.2506.08214

openalex publication_date 2025/06/09 · openalex created_date 2025/10/12 · openalex updated_date 2026/08/02

Abstract

In recent years, the wide availability of high-resolution radar satellite images has enabled the remote monitoring of wetland surface areas. Machine learning models have achieved state-of-the-art results in segmenting wetlands from satellite images. However, these models require large amounts of manually annotated satellite images, which are slow and expensive to produce. The need for annotated training data makes it difficult to adapt these models to changes such as different climates or sensors. To address this issue, we employed self-supervised training methods to develop a model, AquaCluster, which segments radar satellite images into water and land areas without manual annotations. Our final model outperformed other radar-based water detection techniques that do not require annotated data in our test dataset, having achieved a 0.08 improvement in the Intersection over Union metric. Our results demonstrate that it is possible to train machine learning models to detect vegetated water from radar images without the use of annotated data, which can make the retraining of these models to account for changes much easier.

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• Analysis and application of multispectral data for water segmentation using machine learning
• GLH-Water: A Large-Scale Dataset for Global Surface Water Detection in Large-Size Very-High-Resolution Satellite Imagery
• H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain\n Adaptation and Label Refinement