mlsanityData to 14 Oct 2026

Satellite Image Semantic Segmentation

arXiv 2110.05812 ↗First posted 12 Oct 2021cs.CV cs.GRcited by 1

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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.

Key facts. Cited by 1 paper 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

1 of 2 references.

References · 1

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139

Built on by

Cited by year

Citing papers per year: 2021: 1.

1 paper.

  1. 2021Buildings Classification using Very High Resolution Satellite Imagerycited by 0