使用街景全景和集体学习来描绘路上CO2度
Yonglin Zhang1, Tianle Sun2, Li Wang1
1Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
The Science of the total environment
|July 1, 2024
概括
这项研究使用人工智能和街景图像来绘制交通造成的城市二氧化碳 (CO2) 污染的地图. 研究结果表明,垂直种植的树木可以减少二氧化碳,有助于可持续的城市发展.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 人工智能的人工智能
背景情况:
- 交通是城市二氧化碳 (CO2) 排放的主要来源,阻碍了可持续发展.
- 目前对城市地区二氧化碳空间模式的理解受限于缺乏以人为中心的道路接口数据.
- 街道配置,交通和人类活动之间的复杂相互作用创造了复杂的二氧化碳分布模式.
研究的目的:
- 利用街景图像和人工智能开发一种用于测量道路二氧化碳度的新方法.
- 分析街景特征与二氧化碳水平之间的关系.
- 确定高二氧化碳排放区,了解城市规划中的影响因素.
主要方法:
- 在深收集了超过10万对街景全景图 (SVPs) 和二氧化碳度数据.
- 利用四种集体学习 (EL) 模型,在街景特征和二氧化碳之间建立非线性联系.
- 应用人工智能,包括光梯度增强-夏普利添加式扩展 (LGB-SHAP),用于空间分析和特征重要性.
主要成果:
- 在预测二氧化碳度时,预测准确度 (R2) 超过90%,平均绝对误差 (MAE) 低于3.2ppm.
- 创建了一个高分辨率 (100米) 地图,显示了深的平均道路二氧化碳度.
- 确定了高二氧化碳强度的空间集群,并揭示了垂直种植的树木可以减轻道路二氧化碳排放.
结论:
- 街景全景图 (SVPs) 与人工智能 (AI) 结合,提供了一种强大的方法来空间测量道路 CO2.
- 了解影响因素的相互作用和值影响对于有效的城市二氧化碳管理至关重要.
- 该方法支持对可持续城市发展的低排放运输战略的评估和设计.
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