Papers › DSAT-Net: Dual Spatial Attention Transformer for Building Extraction from Aerial Images

DSAT-Net: Dual Spatial Attention Transformer for Building Extraction from Aerial Images

16 Aug 2023IEEE Geoscience and Remote Sensing Letters 2023 8archive 2025-07-28

Renhe Zhang, Zhechun Wan, Qian Zhang, Guixu Zhang

Both local and global context dependencies are essential for building extraction from remote sensing (RS) images. Convolutional Neural Network (CNN) can extract local spatial details well but lacks the ability to model long-range dependency. In recent years, Vision Transformer (ViT) have shown great potential in modeling global context dependency. However, it usually brings huge computational cost, and spatial details can not be fully retained in the process of feature extraction. To maximize the advantages of CNNs and ViTs, we propose DSAT-Net, which combine them in one model. In DSAT-Net, we design an efficient Dual Spatial Attention Transformer (DSAFormer) to solve the defects of standard ViT. It has a dual attention structure to complement each other. Specifically, the global attention path (GAP) conducts a large scale down sampling of the feature maps before the global self-attention computing, to reduce the computational cost. The local attention path (LAP) uses efficient stripe convolution to generate local attention, which can alleviate the loss of information caused by down-sampling operation in the GAP and supplement the spatial details. In addition, we design a feature refining module called Channel Mixing Feature Refine Module (CM-FRM) to fuse low-level and high-level features. Our model achieved competitive results on three public building extraction datasets. Code will be available at: https://github.com/stdcoutzrh/BuildingExtraction.

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Code

stdcoutzrh/BuildingExtraction mentioned in paperpytorch report

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Tasks

Extracting Buildings In Remote Sensing Images Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Extracting Buildings In Remote Sensing Images	Massachusetts building dataset	DSAT-Net	IoU	76.54	#2 of 3	Archive leaderboard	report
Semantic Segmentation	INRIA Aerial Image Labeling	DSAT-Net	IoU	82.68	#5 of 8	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

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

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