# LandCoverNet: A global benchmark land cover classification training dataset

arXiv: https://arxiv.org/abs/2012.03111 · HTML: https://mlsanity.com/paper/2012.03111/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 18 of 18)

- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks](https://mlsanity.com/paper/2112.01715/) (2021)
- [FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery](https://mlsanity.com/paper/2310.13336/) (2023)
- [Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery](https://mlsanity.com/paper/2209.00727/) (2022)
- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark](https://mlsanity.com/paper/2112.00570/) (2021)
- [Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation](https://mlsanity.com/paper/2404.04608/) (2024)
- [FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping](https://mlsanity.com/paper/2506.07080/) (2025)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization](https://mlsanity.com/paper/2508.16272/) (2025)
- [Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation](https://mlsanity.com/paper/2501.04696/) (2025)
- [GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset](https://mlsanity.com/paper/2507.14697/) (2025)
- [Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data](https://mlsanity.com/paper/2201.10985/) (2022)
- [Assessment of Sentinel-2 spatial and temporal coverage based on the scene classification layer](https://mlsanity.com/paper/2406.18584/) (2024)
- [Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps](https://mlsanity.com/paper/2412.00777/) (2024)
- [From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2505.15147/) (2025)
- [BD Open LULC Map: High-resolution land use land cover mapping & benchmarking for urban development in Dhaka, Bangladesh](https://mlsanity.com/paper/2505.21915/) (2025)
- [LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels](https://mlsanity.com/paper/2509.15868/) (2025)

## Abstract (arXiv)

Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to develop land cover classification models. However, such a global application requires a geographically diverse training dataset. Here, we present LandCoverNet, a global training dataset for land cover classification based on Sentinel-2 observations at 10m spatial resolution. Land cover class labels are defined based on annual time-series of Sentinel-2, and verified by consensus among three human annotators.