RegGAN: An End-to-End Network for Building Footprint Generation with Boundary Regularization
Accurate and reliable building footprint maps are of great interest in many applications, e.g., urban monitoring, 3D building modeling, and geographical database updating. When compared to traditional methods, the deep-learning-based semantic segmentation networks have largely boosted the performanc...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI AG
2022-04-01
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| coleção: | Remote Sensing |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/2072-4292/14/8/1835 |
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