Papers › Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network
Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network
wei he, Jiepan Li, Weinan Cao, Liangpei Zhang, Hongyan zhang
Building extraction aims to segment building pixels from remote sensing images and plays an essential role in many applications, such as city planning and urban dynamic monitoring. Over the past few years, deep learning methods with encoder-decoder architectures have achieved remarkable performance due to their powerful feature representation capability. Nevertheless, due to the varying scales and styles of buildings, conventional deep learning models always suffer from uncertain predictions and cannot accurately distinguish the complete footprints of the building from the complex distribution of ground objects, leading to a large degree of omission and commission. In this paper, we realize the importance of uncertain prediction and propose a novel and straightforward Uncertainty-Aware Network (UANet) to alleviate this problem. To verify the performance of our proposed UANet, we conduct extensive experiments on three public building datasets, including the WHU building dataset, the Massachusetts building dataset, and the Inria aerial image dataset. Results demonstrate that the proposed UANet outperforms other state-of-the-art algorithms by a large margin.
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Code
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Tasks
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 | UANet(VGG-16) | IoU | 76.41 | #3 of 3 | Archive leaderboard | report |
| Extracting Buildings In Remote Sensing Images | WHU Building Dataset | UANet(VGG-16) | F1 | 95.91 | #6 of 7 | Archive leaderboard | report |
| Extracting Buildings In Remote Sensing Images | WHU Building Dataset | UANet(VGG-16) | IoU | 92.15 | #6 of 7 | Archive leaderboard | report |
| Semantic Segmentation | INRIA Aerial Image Labeling | UANet(PVT-V2-B2) | IoU | 83.34 | #1 of 8 | Archive leaderboard | report |
| Semantic Segmentation | INRIA Aerial Image Labeling | UANet(Re2sNet50) | IoU | 83.17 | #2 of 8 | Archive leaderboard | report |
| Semantic Segmentation | INRIA Aerial Image Labeling | UANet(VGG-16) | IoU | 83.08 | #3 of 8 | Archive leaderboard | report |
| Semantic Segmentation | INRIA Aerial Image Labeling | UANet(ResNet50) | IoU | 82.17 | #6 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.
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