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

arXiv: https://arxiv.org/abs/2506.08214 · HTML: https://mlsanity.com/paper/2506.08214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
- [Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2105.04951/) (2021)
- [ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands](https://mlsanity.com/paper/1906.00133/) (2019)

## Abstract (arXiv)

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.