Papers › U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery
U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery
Ivica Dimitrovski, Vlatko Spasev, Suzana Loshkovska, Ivan Kitanovski
Semantic segmentation of remote sensing imagery stands as a fundamental task within the domains of both remote sensing and computer vision. Its objective is to generate a comprehensive pixel-wise segmentation map of an image, assigning a specific label to each pixel. This facilitates in-depth analysis and comprehension of the Earth’s surface. In this paper, we propose an approach for enhancing semantic segmentation performance by employing an ensemble of U-Net models with three different backbone networks: Multi-Axis Vision Transformer, ConvFormer, and EfficientNet. The final segmentation maps are generated through a geometric mean ensemble method, leveraging the diverse representations learned by each backbone network. The effectiveness of the base U-Net models and the proposed ensemble is evaluated on multiple datasets commonly used for semantic segmentation tasks in remote sensing imagery, including LandCover.ai, LoveDA, INRIA, UAVid, and ISPRS Potsdam datasets. Our experimental results demonstrate that the proposed approach achieves state-of-the-art performance, showcasing its effectiveness and robustness in accurately capturing the semantic information embedded within remote sensing images.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | ISPRS Potsdam | U-Net (ConvFormer-M36) | Mean IoU | 89.45 | #20 of 20 | Archive leaderboard | report |
| Semantic Segmentation | LandCover.ai | U-Net (ConvFormer-M36) | mIoU | 87.64 | #1 of 1 | Archive leaderboard | report |
| Semantic Segmentation | LoveDA | U-Net (MaxViT-S) | Category mIoU | 56.16 | #1 of 19 | Archive leaderboard | report |
| Semantic Segmentation | UAVid | U-Net Ensemble | Mean IoU | 73.34 | #1 of 10 | Archive leaderboard | report |
| Semantic Segmentation | UAVid | U-Net (MaxViT-S) | Mean IoU | 71.88 | #2 of 10 | 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
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