Papers › Satellite Image Semantic Segmentation

Satellite Image Semantic Segmentation

12 Oct 2021arXiv:2110.05812archive 2025-07-28

Eric Guérin, Killian Oechslin, Christian Wolf, Benoît Martinez

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.

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Code

koechslin/swin-transformer-semantic-segmentation officialmentioned in papermentioned on GitHubpytorchApache-2.0 report

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2D Semantic Segmentation Segmentation Semantic Segmentation

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Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Stochastic Depth Swin Transformer Transformer

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