TopoRF-Net: Topology-Aware Road Segmentation in Multi-Resolution Remote Sensing via Multi-Receptive Field Adaptation
文献类型：期刊论文
| 作者 | Fu, Junjie1; Wang, Chenliang2; Lv, Hongchen1; Lu, Hao1; Shi, Wenjiao3; Liao, Xuefeng2 |
| 刊名 | SENSORS
 |
| 出版日期 | 2025-12-06 |
| 卷号 | 25期号:24页码:7428 |
| 关键词 | remote sensing imagery semantic segmentation road extraction multi-receptive field structural connectivity topology-aware loss |
| DOI | 10.3390/s25247428 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | In multi-resolution remote sensing imagery, roads typically exhibit sparse, elongated, and structurally complex morphological characteristics, posing formidable connectivity modeling challenges for semantic segmentation models. Existing approaches predominantly focus on pixel-level accuracy, often neglecting the topological integrity of road networks, which leads to frequent discontinuities and omissions in predicted results. To address this, this paper proposes an end-to-end road extraction framework equipped with multi-receptive field modeling and structural connectivity preservation capabilities. The model incorporates a multi-receptive-field module to capture road patterns across varying spatial scales, a connectivity-aware decoding mechanism to strengthen structural coherence, and a topology-aware loss that explicitly guides the restoration of continuous road networks during training. On the DeepGlobe-Road dataset, TopoRF-Net achieves OA 98.57%, IoU 69.76%, F1-score 82.18%, Precision 85.50%, and Recall 79.12%; on the Massachusetts dataset, TopoRF-Net similarly achieved outstanding results: OA 96.65%, IoU 59.68%, F1-score 74.75%, Precision 77.98%, and Recall 71.77%. These results conclusively demonstrate that the proposed method significantly outperforms existing approaches in both precision and connectivity metrics, whilst exhibiting favorable parameter efficiency and inference performance. |
| URL标识 | 查看原文 |
| WOS关键词 | EXTRACTION ; NETWORK ; BOUNDARY |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001647376300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219377]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Liao, Xuefeng |
| 作者单位 | 1.SuperMap Software Co Ltd, Beijing 100015, Peoples R China; 2.Wenzhou Univ Technol, Sch Data Sci & Artificial Intelligence, Wenzhou 325000, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fu, Junjie,Wang, Chenliang,Lv, Hongchen,et al. TopoRF-Net: Topology-Aware Road Segmentation in Multi-Resolution Remote Sensing via Multi-Receptive Field Adaptation[J]. SENSORS,2025,25(24):7428. |
| APA | Fu, Junjie,Wang, Chenliang,Lv, Hongchen,Lu, Hao,Shi, Wenjiao,&Liao, Xuefeng.(2025).TopoRF-Net: Topology-Aware Road Segmentation in Multi-Resolution Remote Sensing via Multi-Receptive Field Adaptation.SENSORS,25(24),7428. |
| MLA | Fu, Junjie,et al."TopoRF-Net: Topology-Aware Road Segmentation in Multi-Resolution Remote Sensing via Multi-Receptive Field Adaptation".SENSORS 25.24(2025):7428. |
入库方式： OAI收割
来源：地理科学与资源研究所
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