Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images
Road extraction from UAV-based remote sensing images in rural areas presents significant challenges due to the diverse and complex characteristics of rural roads. Additionally, acquiring UAV remote sensing data for rural areas is challenging due to the high cost of equipment, the lack of clear road...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1394 |
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| author | Jian Wang Renlong Wang Yahui Liu Fei Zhang Ting Cheng |
| author_facet | Jian Wang Renlong Wang Yahui Liu Fei Zhang Ting Cheng |
| author_sort | Jian Wang |
| collection | DOAJ |
| description | Road extraction from UAV-based remote sensing images in rural areas presents significant challenges due to the diverse and complex characteristics of rural roads. Additionally, acquiring UAV remote sensing data for rural areas is challenging due to the high cost of equipment, the lack of clear road boundaries requiring extensive manual annotation, and limited regional policy support for UAV operations. To address these challenges, we propose a transferable contextual network (TCNet), designed to enhance the transferability and accuracy of rural road extraction. We employ a Stable Diffusion model for data augmentation, generating diverse training samples and providing a new method for acquiring remote sensing images. TCNet integrates the clustered contextual Transformer (CCT) module, clustered cross-attention (CCA) module, and CBAM attention mechanism to ensure efficient model transferability across different geographical and climatic conditions. Moreover, we design a new loss function, the Dice-BCE-Lovasz loss (DBL loss), to accelerate convergence and improve segmentation performance in handling imbalanced data. Experimental results demonstrate that TCNet, with only 23.67 M parameters, performs excellently on the DeepGlobe and road datasets and shows outstanding transferability in zero-shot testing on rural remote sensing data. TCNet performs well on segmentation tasks without any fine-tuning for regions such as Burgundy, France, and Yunnan, China. |
| format | Article |
| id | doaj-art-7218e9a4214a40519c3cb990e5daafd2 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-7218e9a4214a40519c3cb990e5daafd22025-08-20T02:59:15ZengMDPI AGSensors1424-82202025-02-01255139410.3390/s25051394Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing ImagesJian Wang0Renlong Wang1Yahui Liu2Fei Zhang3Ting Cheng4Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, ChinaRoad extraction from UAV-based remote sensing images in rural areas presents significant challenges due to the diverse and complex characteristics of rural roads. Additionally, acquiring UAV remote sensing data for rural areas is challenging due to the high cost of equipment, the lack of clear road boundaries requiring extensive manual annotation, and limited regional policy support for UAV operations. To address these challenges, we propose a transferable contextual network (TCNet), designed to enhance the transferability and accuracy of rural road extraction. We employ a Stable Diffusion model for data augmentation, generating diverse training samples and providing a new method for acquiring remote sensing images. TCNet integrates the clustered contextual Transformer (CCT) module, clustered cross-attention (CCA) module, and CBAM attention mechanism to ensure efficient model transferability across different geographical and climatic conditions. Moreover, we design a new loss function, the Dice-BCE-Lovasz loss (DBL loss), to accelerate convergence and improve segmentation performance in handling imbalanced data. Experimental results demonstrate that TCNet, with only 23.67 M parameters, performs excellently on the DeepGlobe and road datasets and shows outstanding transferability in zero-shot testing on rural remote sensing data. TCNet performs well on segmentation tasks without any fine-tuning for regions such as Burgundy, France, and Yunnan, China.https://www.mdpi.com/1424-8220/25/5/1394remote sensingrural road extractionsemantic segmentationStable Diffusion |
| spellingShingle | Jian Wang Renlong Wang Yahui Liu Fei Zhang Ting Cheng Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images Sensors remote sensing rural road extraction semantic segmentation Stable Diffusion |
| title | Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images |
| title_full | Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images |
| title_fullStr | Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images |
| title_full_unstemmed | Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images |
| title_short | Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images |
| title_sort | transferable contextual network for rural road extraction from uav based remote sensing images |
| topic | remote sensing rural road extraction semantic segmentation Stable Diffusion |
| url | https://www.mdpi.com/1424-8220/25/5/1394 |
| work_keys_str_mv | AT jianwang transferablecontextualnetworkforruralroadextractionfromuavbasedremotesensingimages AT renlongwang transferablecontextualnetworkforruralroadextractionfromuavbasedremotesensingimages AT yahuiliu transferablecontextualnetworkforruralroadextractionfromuavbasedremotesensingimages AT feizhang transferablecontextualnetworkforruralroadextractionfromuavbasedremotesensingimages AT tingcheng transferablecontextualnetworkforruralroadextractionfromuavbasedremotesensingimages |