SAM2-RoadNet: Topology-Aware Multi-Scale Road Extraction from High-Resolution Remote Sensing Images.
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| Title: | SAM2-RoadNet: Topology-Aware Multi-Scale Road Extraction from High-Resolution Remote Sensing Images. |
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| Authors: | Feng, Ruyue1 (AUTHOR), Guo, Ziyou1,2 (AUTHOR), Du, Xiao1,2 (AUTHOR), Wu, Tieru1,2 (AUTHOR) wutr@jlu.edu.cn |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p913. 23p. |
| Subjects: | Road maps, Multiple scale method, Remote sensing, Knowledge transfer, Deep learning, Image segmentation, Data fusion (Statistics) |
| Abstract: | Highlights: What are the main findings? A topology-aware multi-scale road extraction framework (SAM2-RoadNet) is proposed by adapting the SAM2 image encoder to high-resolution remote sensing imagery. The combination of adapter-based domain adaptation, receptive field blocks, and a weighted BiFPN significantly improves road continuity and segmentation accuracy. What are the implication of the main findings? The proposed method effectively reduces road fragmentation and preserves topological connectivity in complex remote sensing scenes. The study demonstrates that large vision foundation models can be efficiently transferred to remote sensing tasks with strong robustness and cross-dataset generalization. Road extraction from high-resolution remote sensing images (HRSIs) is a fundamental task for many geospatial applications, yet it remains challenging due to complex backgrounds, frequent occlusions, and the requirement to preserve the topological connectivity of elongated road networks. To address these issues, this paper proposes SAM2-RoadNet, a topology-aware multi-scale road extraction framework that adapts the powerful representation capability of the Segment Anything Model 2 (SAM2) to HRSI road segmentation. Unlike prompt-driven segmentation paradigms, SAM2-RoadNet employs the SAM2 image encoder solely as a feature extractor and introduces an adapter-based domain adaptation strategy to efficiently transfer pretrained knowledge to the remote sensing domain. Receptive field blocks are further integrated to enhance contextual perception and align channel dimensions, followed by a weighted bidirectional feature pyramid network (W-BiFPN) to fuse hierarchical features across multiple scales. Moreover, a topology-aware training strategy based on the soft-clDice loss is incorporated to explicitly enforce structural continuity and reduce road fragmentation. Extensive experiments conducted on two challenging benchmarks, including DeepGlobe, Massachusetts, demonstrate that SAM2-RoadNet achieves superior overall performance across multiple evaluation metrics compared with state-of-the-art methods in both quantitative accuracy and qualitative visual quality, while demonstrating promising cross-dataset transferability without additional fine-tuning. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A topology-aware multi-scale road extraction framework (SAM2-RoadNet) is proposed by adapting the SAM2 image encoder to high-resolution remote sensing imagery. The combination of adapter-based domain adaptation, receptive field blocks, and a weighted BiFPN significantly improves road continuity and segmentation accuracy. What are the implication of the main findings? The proposed method effectively reduces road fragmentation and preserves topological connectivity in complex remote sensing scenes. The study demonstrates that large vision foundation models can be efficiently transferred to remote sensing tasks with strong robustness and cross-dataset generalization. Road extraction from high-resolution remote sensing images (HRSIs) is a fundamental task for many geospatial applications, yet it remains challenging due to complex backgrounds, frequent occlusions, and the requirement to preserve the topological connectivity of elongated road networks. To address these issues, this paper proposes SAM2-RoadNet, a topology-aware multi-scale road extraction framework that adapts the powerful representation capability of the Segment Anything Model 2 (SAM2) to HRSI road segmentation. Unlike prompt-driven segmentation paradigms, SAM2-RoadNet employs the SAM2 image encoder solely as a feature extractor and introduces an adapter-based domain adaptation strategy to efficiently transfer pretrained knowledge to the remote sensing domain. Receptive field blocks are further integrated to enhance contextual perception and align channel dimensions, followed by a weighted bidirectional feature pyramid network (W-BiFPN) to fuse hierarchical features across multiple scales. Moreover, a topology-aware training strategy based on the soft-clDice loss is incorporated to explicitly enforce structural continuity and reduce road fragmentation. Extensive experiments conducted on two challenging benchmarks, including DeepGlobe, Massachusetts, demonstrate that SAM2-RoadNet achieves superior overall performance across multiple evaluation metrics compared with state-of-the-art methods in both quantitative accuracy and qualitative visual quality, while demonstrating promising cross-dataset transferability without additional fine-tuning. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060913 |
