# Satellite Image Semantic Segmentation

arXiv: https://arxiv.org/abs/2110.05812 · HTML: https://mlsanity.com/paper/2110.05812/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 1 of 1)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)

## Built on by (most cited first, 1 of 1)

- [Buildings Classification using Very High Resolution Satellite Imagery](https://mlsanity.com/paper/2111.14650/) (2021)

## Abstract (arXiv)

In this paper, we propose a method for the automatic semantic segmentation of satellite images into six classes (sparse forest, dense forest, moor, herbaceous formation, building, and road). We rely on Swin Transformer architecture and build the dataset from IGN open data. We report quantitative and qualitative segmentation results on this dataset and discuss strengths and limitations. The dataset and the trained model are made publicly available.