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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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