# Large-scale Land Cover Classification in GaoFen-2 Satellite Imagery

arXiv: https://arxiv.org/abs/1806.00901 · HTML: https://mlsanity.com/paper/1806.00901/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2412.19492/) (2024)
- [Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2112.05220/) (2021)
- [GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset](https://mlsanity.com/paper/2507.14697/) (2025)
- [Earth Science Foundation Models: From Perception to Reasoning and Discovery](https://mlsanity.com/paper/2605.12542/) (2026)

## Abstract (arXiv)

Many significant applications need land cover information of remote sensing images that are acquired from different areas and times, such as change detection and disaster monitoring. However, it is difficult to find a generic land cover classification scheme for different remote sensing images due to the spectral shift caused by diverse acquisition condition. In this paper, we develop a novel land cover classification method that can deal with large-scale data captured from widely distributed areas and different times. Additionally, we establish a large-scale land cover classification dataset consisting of 150 Gaofen-2 imageries as data support for model training and performance evaluation. Our experiments achieve outstanding classification accuracy compared with traditional methods.