可感知的景观图案揭示了城市不可见的社会经济形象
Wenning Li1, Ranhao Sun2, Hongbin He2
1State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Science bulletin
|July 5, 2024
概括
本研究介绍了四个新的3D城市景观指标 (UL3D),这些指标来自街景图像. 这些UL3D指标显著提高了城市社会经济概况的预测,提供了超越传统二维分析的新视角.
科学领域:
- 城市研究是城市研究.
- 地理空间分析是什么?
- 深度学习应用程序深度学习应用程序
背景情况:
- 城市景观评估对于城市可持续发展至关重要.
- 传统的远程传感的二维景观指标缺乏三维空间信息.
- 获取大面积的3D数据和开发评估指标是具有挑战性的.
研究的目的:
- 提出和验证四个新的三维城市景观指标 (UL3D):绿色,灰色,开放和拥挤.
- 评估UL3D与2D指标相比的区分能力.
- 评估UL3D在预测城市社会经济概况方面的有效性.
主要方法:
- 利用了来自中国303个城市的403万张街景图像.
- 使用深度学习方法来构建UL3D指标.
- 集成UL3D与城市背景和2D指标在回归模型中预测社会经济数据.
主要成果:
- UL3D指标与2D指标的相关性很低 (0.31),突出了它们独特的信息.
- 在2018-2019年左右,城市景观动态的转变被观察到,灰色/拥挤程度放缓,开放性增加.
- 纳入UL3D显著提高了人均GDP (25.0%),城市人口率 (19.8%),人均建筑面积 (35.5%) 和医院数量 (19.2%) 的预测准确度.
结论:
- 三维城市景观指标提供了与二维指标不同的有价值的见解.
- UL3D指标是城市社会经济条件的有效预测指标.
- 这种方法为全面的城市景观评估和社会经济分析提供了一种新方法.
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