通过计算机视觉和多模型机器学习,减少洛杉矶社区视觉环境的社会不平等
Siqin Wang1,2,3, Jooyoung Yoo1, Wenhui Cai2
1Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, US.
Sustainable cities and society
|February 3, 2025
概括
这项研究揭示了社区观念中的社会不平等,发现少数民族和低收入人口度较高的地区被认为是不那么理想的. 城市绿地和住房密度显著影响人们对环境的感知.
科学领域:
- 城市规划和环境管理
- 公共卫生 公共卫生
- 计算机视觉和地理空间分析
背景情况:
- 城市环境对公民福祉和社会公平有重大影响.
- 可持续发展目标强调安全,健康和包容性城市.
- 了解建筑环境和感知到的社区质量之间的联系对于城市规划至关重要.
研究的目的:
- 调查不同社会群体在公民福祉和邻居感知方面的社会不平等.
- 用5D维度 (密度,多样性,设计,距离,目的地) 来量化建筑环境.
- 通过计算机视觉分析建筑环境特征与感知邻里视觉质量之间的关联.
主要方法:
- 使用众包街景图像 (Mapillary) 和计算机视觉技术.
- 量化建筑环境特征和六种类型的邻居视觉感知 (美丽,安全,富裕,宜居,无聊,令人丧).
- 采用多模型机器学习来识别影响知觉的关键环境特征.
主要成果:
- 黑人,西班牙裔,低收入,低教育和失业人口度较高的社区被认为不那么美丽,不那么宜居,不那么安全和不那么富有.
- 影响感知的关键积极环境特征包括树冠密度,多单位密度,距离CBD的距离和汽车通勤.
- 调查结果在不同社会群体中是一致的.
结论:
- 确定了与建筑环境相关的邻居感知中的社会不平等的基于地点的证据.
- 强调城市设计元素的重要性,如绿色空间和住房密度,以提高感知到的社区质量.
- 框架和方法为城市规划和健康城市倡议提供了宝贵的工具.
相关概念视频
01:24Vision
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01:15Depth Perception and Spatial Vision
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01:18Reducing Line Loss
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With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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02:16Light Acquisition
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