# GLH-Water: A Large-Scale Dataset for Global Surface Water Detection in Large-Size Very-High-Resolution Satellite Imagery

arXiv: https://arxiv.org/abs/2303.09310 · HTML: https://mlsanity.com/paper/2303.09310/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 65 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [Rethinking BiSeNet For Real-time Semantic Segmentation](https://mlsanity.com/paper/2104.13188/) (2021)
- [Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models](https://mlsanity.com/paper/1807.05713/) (2018)
- [OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping](https://mlsanity.com/paper/2210.10732/) (2022)
- [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022)
- [Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis](https://mlsanity.com/paper/2001.01306/) (2020)
- [LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery](https://mlsanity.com/paper/2005.02264/) (2020)
- [Progressive Semantic Segmentation](https://mlsanity.com/paper/2104.03778/) (2021)
- [Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery](https://mlsanity.com/paper/2209.00727/) (2022)
- [Peripheral Vision Transformer](https://mlsanity.com/paper/2206.06801/) (2022)
- [Deep Learning Models for River Classification at Sub-Meter Resolutions from Multispectral and Panchromatic Commercial Satellite Imagery](https://mlsanity.com/paper/2212.13613/) (2022)

## Built on by (most cited first, 16 of 16)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery](https://mlsanity.com/paper/2312.02481/) (2023)
- [SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing](https://mlsanity.com/paper/2412.19237/) (2024)
- [UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction](https://mlsanity.com/paper/2502.15199/) (2025)
- [HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery](https://mlsanity.com/paper/2507.16251/) (2025)
- [Earth System Data Cubes: Avenues for advancing Earth system research](https://mlsanity.com/paper/2408.02348/) (2024)
- [Urban Socio-Semantic Segmentation with Vision-Language Reasoning](https://mlsanity.com/paper/2601.10477/) (2026)
- [SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation](https://mlsanity.com/paper/2504.19839/) (2025)
- [SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking](https://mlsanity.com/paper/2409.17087/) (2024)
- [Flood Data Analysis on SpaceNet 8 Using Apache Sedona](https://mlsanity.com/paper/2404.18235/) (2024)
- [RSRWKV: A Linear-Complexity 2D Attention Mechanism for Efficient Remote Sensing Vision Task](https://mlsanity.com/paper/2503.20382/) (2025)
- [KunPeng: A Global Ocean Environmental Model](https://mlsanity.com/paper/2504.04766/) (2025)
- [FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2603.07468/) (2026)
- [PolyTopoBench: A Benchmark for Complex Vector Polygon Generation from Remote Sensing Imagery](https://mlsanity.com/paper/2609.32856/) (2026)
- [VesselBench-800K: A Large-scale Perception Benchmark for Multimodal Vessel Detection, Counting, and Density Estimation](https://mlsanity.com/paper/2609.37003/) (2026)
- [Label Less, Learn More: Resource-Efficient Active Semi-Supervised Learning for Onboard Satellite Image Annotation](https://mlsanity.com/paper/2609.37481/) (2026)

## Abstract (arXiv)

Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment. Although achievements have been made in detecting surface water in small-size satellite images corresponding to local geographic scales, datasets and methods suitable for mapping and analyzing global surface water have yet to be explored. To encourage the development of this task and facilitate the implementation of relevant applications, we propose the GLH-water dataset that consists of 250 satellite images and manually labeled surface water annotations that are distributed globally and contain water bodies exhibiting a wide variety of types (e.g., rivers, lakes, and ponds in forests, irrigated fields, bare areas, and urban areas). Each image is of the size 12,800 × 12,800 pixels at 0.3 meter spatial resolution. To build a benchmark for GLH-water, we perform extensive experiments employing representative surface water detection models, popular semantic segmentation models, and ultra-high resolution segmentation models. Furthermore, we also design a strong baseline with the novel pyramid consistency loss (PCL) to initially explore this challenge. Finally, we implement the cross-dataset and pilot area generalization experiments, and the superior performance illustrates the strong generalization and practical application of GLH-water. The dataset is available at https://jack-bo1220.github.io/project/GLH-water.html.