Satellite Image Semantic Segmentation

2021/10/12 by Éric Guérin, Guérin, Eric, Killian Oechslin +5 · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Geochemistry and Geologic Mapping #Graphics (cs.GR) #I.4.6

paper · pdf · doi:10.48550/arxiv.2110.05812

openalex publication_date 2021/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Cited by

• Buildings Classification using Very High Resolution Satellite Imagery

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