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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2408.05891 |
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| _version_ | 1866909301996519424 |
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| author | Zhang, Yecheng Zhao, Huimin Long, Ying |
| author_facet | Zhang, Yecheng Zhao, Huimin Long, Ying |
| contents | Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Current building datasets suffer from incomplete coverage of building multi-attributes. This paper introduces a geospatial artificial intelligence (GeoAI) framework for large-scale building modeling, presenting the first national-scale Multi-Attribute Building dataset (CMAB), covering 3,667 spatial cities, 29 million buildings, and 21.3 billion square meters of rooftops with an F1-Score of 89.93% in OCRNet-based extraction, totaling 337.7 billion cubic meters of building stock. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating features such as morphology, location, and function. Using multi-source data, including billions of high-resolution Google Earth images and 60 million street view images (SVIs), we generated rooftop, height, function, age, and quality attributes for each building. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation, mostly above 80%. Our dataset and results are crucial for global SDGs and urban planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05891 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CMAB: A First National-Scale Multi-Attribute Building Dataset in China Derived from Open Source Data and GeoAI Zhang, Yecheng Zhao, Huimin Long, Ying Computer Vision and Pattern Recognition I.4.9 Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Current building datasets suffer from incomplete coverage of building multi-attributes. This paper introduces a geospatial artificial intelligence (GeoAI) framework for large-scale building modeling, presenting the first national-scale Multi-Attribute Building dataset (CMAB), covering 3,667 spatial cities, 29 million buildings, and 21.3 billion square meters of rooftops with an F1-Score of 89.93% in OCRNet-based extraction, totaling 337.7 billion cubic meters of building stock. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating features such as morphology, location, and function. Using multi-source data, including billions of high-resolution Google Earth images and 60 million street view images (SVIs), we generated rooftop, height, function, age, and quality attributes for each building. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation, mostly above 80%. Our dataset and results are crucial for global SDGs and urban planning. |
| title | CMAB: A First National-Scale Multi-Attribute Building Dataset in China Derived from Open Source Data and GeoAI |
| topic | Computer Vision and Pattern Recognition I.4.9 |
| url | https://arxiv.org/abs/2408.05891 |