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预测户外活动游戏的物理环境特征可以使用谷歌街景图像进行测量
Randy Boyes1,2, William Pickett3,4, Ian Janssen3,5
1Department of Public Health Sciences, Queen's University, Kingston, ON, K7L 3N6, Canada. rboyes@presagegroup.com.
International journal of health geographics
|September 27, 2023
概括
使用谷歌街景图像的机器学习模型可以测量儿童积极玩的环境特征. 虽然对稳定的特征有效,但在新位置应用时,对动态元素的性能有所不同.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 计算机科学 计算机科学
背景情况:
- 儿童户外活动的游戏对儿童的发展至关重要.
- 游戏行为受到物理和社会环境特征的影响.
- 传统的数据源很难捕捉这些细微的环境特征.
研究的目的:
- 评估使用机器学习与谷歌街景 (GSV) 图像的可行性.
- 测量各种与游戏相关的物理和社会环境特征.
- 评估这些模型在不同城市环境中的通用性.
主要方法:
- 开发机器学习模型来检测自然特征,交通 (行人,车辆,自行车),交通信号和人行道,使用GSV图像.
- 在一个城市训练模特,并在第二个不同的城市验证他们的表现.
- 对时间不变与时间变化的环境特征进行模型性能比较.
主要成果:
- 模型对于时间不变的特征,如人行道和自然元素,表现良好.
- 当模型在培训环境之外应用时,对时间变化的特征 (如交通) 的性能显著下降,例如交通.
- 在测试新城市环境中的模型时,观察到普遍性挑战.
结论:
- GSV图像为环境特征测量提供了一个潜在的自动化数据源.
- 应用于GSV数据的机器学习可以支持游戏行为预测模型的开发.
- 需要进行进一步的研究,以提高动态环境特征和跨城市应用的模型稳定性.
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