Papers › SDSC-UNet: Dual Skip Connection ViT-based U-shaped Model for Building Extraction

SDSC-UNet: Dual Skip Connection ViT-based U-shaped Model for Building Extraction

25 Apr 2023IEEE Geoscience and Remote Sensing Letters 2023 4archive 2025-07-28

Renhe Zhang, Qian Zhang, Guixu Zhang

Benefiting from effective global information interaction, vision-transformers (ViTs) have been widely used in the building extraction task. However, buildings in remote sensing (RS) images usually differ greatly in size. Mainstream ViT-based segmentation models for RS images are based on Swin Transformer, which lacks multi-scale information inside the ViT block. In addition, they only connect the output of the entire ViT encoder block to the decoder, which ignore the similarity information of the attention maps inside the ViT encoder block, and are unable to provide better global dependencies for the decoder. To solve above problems, we introduce a novel Shunted Transformer, which enables the model to capture multi-scale information internally while fully establishing global dependencies, to build a pure ViT-based U-shaped model for building extraction. Furthermore, unlike the previous single-skip-connection structure of U-shaped methods, we build a novel dual skip connection structure inside the model. It simultaneously transmits the attention maps inside the ViT encoder block and its entire output to the decoder, thereby fully mining the information of the ViT encoder block and providing better global information guidance for the decoder. Thus, our model is named Shunted Dual Skip Connection UNet (SDSC-UNet). We also design a feature fusion module called Dual Skip Upsample Fusion Module (DSUFM) to aggregate the information. Our model has yields state-of-the-art (SOTA) performance (83.02%IoU) on the Inria Aerial Image Labeling Dataset. 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

Decoder 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	SDSC-UNet	IoU	76.71	#1 of 3	Archive leaderboard	report
Semantic Segmentation	INRIA Aerial Image Labeling	SDSC-UNet	IoU	83.01	#4 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 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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