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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2408.14400 |
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| _version_ | 1866929478182109184 |
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| author | Batchu, Vishal Wilson, Alex Peng, Betty Elkin, Carl Jain, Umangi Van Arsdale, Christopher Goroshin, Ross Gulshan, Varun |
| author_facet | Batchu, Vishal Wilson, Alex Peng, Betty Elkin, Carl Jain, Umangi Van Arsdale, Christopher Goroshin, Ross Gulshan, Varun |
| contents | The transition to renewable energy, particularly solar, is key to mitigating climate change. Google's Solar API aids this transition by estimating solar potential from aerial imagery, but its impact is constrained by geographical coverage. This paper proposes expanding the API's reach using satellite imagery, enabling global solar potential assessment. We tackle challenges involved in building a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views using deep learning models. Our models, trained on aligned satellite and aerial datasets, produce 25cm DSMs and roof segments. With ~1m DSM MAE on buildings, ~5deg roof pitch error and ~56% IOU on roof segmentation, they significantly enhance the Solar API's potential to promote solar adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14400 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping Batchu, Vishal Wilson, Alex Peng, Betty Elkin, Carl Jain, Umangi Van Arsdale, Christopher Goroshin, Ross Gulshan, Varun Computer Vision and Pattern Recognition Machine Learning The transition to renewable energy, particularly solar, is key to mitigating climate change. Google's Solar API aids this transition by estimating solar potential from aerial imagery, but its impact is constrained by geographical coverage. This paper proposes expanding the API's reach using satellite imagery, enabling global solar potential assessment. We tackle challenges involved in building a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views using deep learning models. Our models, trained on aligned satellite and aerial datasets, produce 25cm DSMs and roof segments. With ~1m DSM MAE on buildings, ~5deg roof pitch error and ~56% IOU on roof segmentation, they significantly enhance the Solar API's potential to promote solar adoption. |
| title | Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2408.14400 |