23个城市的OpenStreetMap衍生多式联运数据集:与生物气候变量配对的城市形态瓦片
1School of Civil Engineering, Liaoning Technical University, Fuxin, Liaoning Province, China.
Data in brief
|February 18, 2026
概括
来自OpenStreetMap的新多式联络数据集涵盖了23个城市,提供了对齐的生态特征图像对和城市形态图. 该资源有助于机器学习用于城市设计和比较气候研究.
科学领域:
- 地理空间数据科学是什么?
- 城市形态学研究城市形态学研究.
- 环境信息学环境信息学
背景情况:
- 城市规划和设计需要详细的空间数据进行分析和建模.
- 现有的数据集往往缺乏多式联运对齐或全面的生态和功能信息.
- 城市设计的生成模型需要多样化,大规模的数据集用于培训和验证.
研究的目的:
- 为城市形态学和生态分析引入一个新的,多式联运的OpenStreetMap衍生数据集.
- 提供对齐的图像对,代表生态基线和不同城市的功能性城市地图.
- 促进机器学习在城市设计,比较形态学和数据归算中的应用.
主要方法:
- 从23个城市的OpenStreetMap数据创建数据集,包括11711个层样本.
- 对齐图像对的生成:风格化的生态基线和功能性城市形态地图.
- 包括地质信息和生物气候变量,并提供用于重建的脚本.
- 按城市进行组织,并使用JSONL记录进行索引,以集成机器学习.
主要成果:
- 一个全面的多式联运数据集,包含23个城市的11,711个样本.
- 对齐的图像对详细说明了生态特征 (绿色/水,交通) 和城市功能类.
- 生物气候变量和地质数据的整合,用于气候意识分析.
- 数据集结构优化,可直接用于机器学习管道.
结论:
- 该数据集使城市设计的先进机器学习成为可能,包括生成建模.
- 促进城市形态学的跨城市和跨气候的比较分析.
- 支持在数据稀缺地区对功能性建筑和基础设施数据的归算.
- 生态基线作为建模的一般化城市前模板.
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