# Satellite Image Semantic Segmentation

Source: [Satellite Image Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6af61e209131cea2b62c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:37:16.577Z

Authors: Eric Gu\\'erin, Killian Oechslin, Christian Wolf, Beno\\^it Martinez

Published: 2021-10-12T08:14:52.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2110.05812>)

[PDF](<https://arxiv.org/pdf/2110.05812.pdf>)