用街景图像和深度学习,对人类对中国城市财富和身体障碍的看法进行地理空间数据集
Yanji Zhang1, Yongyi You2, Shaokai Chen3
1Department of Sociology, School of Humanities and Social Sciences, Fuzhou University. No. 2 North Wulongjiang Ave, Fuzhou 350108, China.
Data in brief
|October 23, 2025
概括
中国街景图像的新数据集提供了对社区社会经济状况的见解. 这些数据集使人工智能模型能够分析中国城市的感知财富和身体障碍.
科学领域:
- 城市研究是城市研究.
- 社会经济分析 社会经济分析
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 人类对环境质量的看法是社区社会经济地位的关键指标.
- 现有的模型缺乏当地的中国街景数据和注释器.
- 在中国需要精细的社会空间数据.
研究的目的:
- 使用街景图像创建新的数据集,用于分析中国的社会经济状况.
- 训练人工智能模型来预测感知到的财富和身体障碍.
- 支持中国的广泛,精细的社会空间研究.
主要方法:
- 数据集I:4万张中国街景图像,由当地城市规划人员使用图像比较进行注释.
- 在数据集I上训练图像回归模型.
- 数据集II:预测中国城市3600多万张街景图像 (2013-2022) 的感知财富和身体障碍得分.
主要成果:
- 数据集I (注释) 和数据集II (预测) 的发展.
- 数据集II包括图像,网格单元和行政区域层面的数据.
- 这些数据集涵盖了2013年至2022年的中国城市.
结论:
- 创建的数据集解决了中国本地化AI感知模型的差距.
- 这些资源有助于研究不平等,种族隔离, gentrification,犯罪和体力活动.
- 能够在中国城市进行大规模的数据驱动的社会空间分析.
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