RU-Net++: An automatic extraction method for Impervious Surface Area based on neural networks
Impervious Surface Area (ISA) is vital for urban planning, environmental monitoring, and water management. Traditional remote sensing methods struggle with complex urban landscapes, leading to accuracy limitations. To address this, we propose RU-Net++, a deep learning-based ISA extraction model inte...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1655/2025/isprs-archives-XLVIII-G-2025-1655-2025.pdf |
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| _version_ | 1849694115224092672 |
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| author | F. Yu X. Tu L. Cai J. Zhang Z. Wang |
| author_facet | F. Yu X. Tu L. Cai J. Zhang Z. Wang |
| author_sort | F. Yu |
| collection | DOAJ |
| description | Impervious Surface Area (ISA) is vital for urban planning, environmental monitoring, and water management. Traditional remote sensing methods struggle with complex urban landscapes, leading to accuracy limitations. To address this, we propose RU-Net++, a deep learning-based ISA extraction model integrating ResNet50 as the encoder with spatial, channel, and dual attention mechanisms. The decoder employs an Atrous Spatial Pyramid Pooling (ASPP) module and multiple refinement modules to enhance feature representation and edge restoration. Trained on GLC_FCS30D and GISA datasets, RU-Net++ outperforms traditional methods in IoU, F1 Score, and Overall Accuracy, offering a reliable tool for sustainable urban development and land-use management. |
| format | Article |
| id | doaj-art-2d6c38d1bfba4739ba7af0ba9a404acb |
| institution | DOAJ |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-2d6c38d1bfba4739ba7af0ba9a404acb2025-08-20T03:20:12ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-08-01XLVIII-G-20251655166210.5194/isprs-archives-XLVIII-G-2025-1655-2025RU-Net++: An automatic extraction method for Impervious Surface Area based on neural networksF. Yu0X. Tu1L. Cai2J. Zhang3Z. Wang4School of Surveying, Mapping and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Surveying, Mapping and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Surveying, Mapping and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Surveying, Mapping and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Surveying, Mapping and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, ChinaImpervious Surface Area (ISA) is vital for urban planning, environmental monitoring, and water management. Traditional remote sensing methods struggle with complex urban landscapes, leading to accuracy limitations. To address this, we propose RU-Net++, a deep learning-based ISA extraction model integrating ResNet50 as the encoder with spatial, channel, and dual attention mechanisms. The decoder employs an Atrous Spatial Pyramid Pooling (ASPP) module and multiple refinement modules to enhance feature representation and edge restoration. Trained on GLC_FCS30D and GISA datasets, RU-Net++ outperforms traditional methods in IoU, F1 Score, and Overall Accuracy, offering a reliable tool for sustainable urban development and land-use management.https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1655/2025/isprs-archives-XLVIII-G-2025-1655-2025.pdf |
| spellingShingle | F. Yu X. Tu L. Cai J. Zhang Z. Wang RU-Net++: An automatic extraction method for Impervious Surface Area based on neural networks The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | RU-Net++: An automatic extraction method for Impervious Surface Area based on neural networks |
| title_full | RU-Net++: An automatic extraction method for Impervious Surface Area based on neural networks |
| title_fullStr | RU-Net++: An automatic extraction method for Impervious Surface Area based on neural networks |
| title_full_unstemmed | RU-Net++: An automatic extraction method for Impervious Surface Area based on neural networks |
| title_short | RU-Net++: An automatic extraction method for Impervious Surface Area based on neural networks |
| title_sort | ru net an automatic extraction method for impervious surface area based on neural networks |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1655/2025/isprs-archives-XLVIII-G-2025-1655-2025.pdf |
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