CMAB:中国的多属性建筑数据集
Yecheng Zhang1, Huimin Zhao1, Ying Long2,3
1School of Architecture, Tsinghua University, Beijing, 100084, China.
Scientific data
|March 13, 2025
概括
本研究介绍了第一个国家多属性建筑数据集 (CMAB),利用人工智能提取详细的建筑信息. 综合数据集可以提高城市分析和规划的准确性.
科学领域:
- 地理信息学是一种地理信息学.
- 城市分析城市分析.
- 人工智能的人工智能
背景情况:
- 准确的3D建筑数据对于城市分析,模拟和政策至关重要,但目前的数据集缺乏全面的多属性覆盖.
- 现有的建筑数据集往往具有不完整的几何和指示属性,限制了它们对详细城市研究的有用性.
研究的目的:
- 提出第一个国家级的多属性建筑数据集 (CMAB) 带有人工智能驱动的全面建筑信息.
- 为准确的城市分析,模拟,政策更新和全球可持续发展目标 (SDGs) 提供有价值的资源.
主要方法:
- 使用人工智能和机器学习开发了一个全国范围的数据集 (CMAB),涵盖3,667个城市和3100万个建筑物.
- 使用OCRNet进行属性提取 (F1-Score 89.93%) 和引导集成的XGBoost模型,包括形态,位置和功能.
- 利用多源数据,包括遥感和街景图像 (SVIs),生成屋顶,高度,结构,功能,风格,年龄和质量属性.
主要成果:
- 创建了一个全面的国家建筑数据集 (CMAB),具有广泛的几何和指示属性.
- 在属性提取方面实现了高准确性,OCRNet的F1-Score为89.93%,一般超过80%的验证准确性.
- 3630亿立方米的量化建筑库,为城市研究提供了前所未有的细节.
结论:
- CMAB数据集是城市规划和分析的重大进步,解决了以前数据集的局限性.
- 由人工智能驱动的方法论展示了一个可扩展和准确的方法来生成丰富的建筑信息.
- 这一数据集对于支持全球可持续发展目标和明智的城市发展战略至关重要.
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