本期为大家推荐的内容为论文《Protocol for assessing neighborhood physical disorder using the YOLOv8 deep learning model》（使用YOLOv8深度学习模型评估邻里空间失序的详细研究方案），受邀发表在 Cell旗下 STAR Protocols 期刊，欢迎大家学习与交流。
题目：Protocol for assessing neighborhood physical disorder using the YOLOv8 deep learning model
（使用YOLOv8深度学习模型评估邻里空间失序的详细研究方案）
作者：
Yan Li*, Yue Ma and Ying Long*
发表刊物：
STAR Protocols
DOI:
https://doi.org/10.1016/j.xpro.2023.102778
引用格式：
Li, Y., Ma, Y., & Long, Y. (2024). Protocol for assessing neighborhood physical disorder using the YOLOv8 deep learning model. STAR protocols, 5(1), 102778.
摘要ABSTRACT
Neighborhood physical disorder (PD), characterized by disruptions and irregularities in spatial elements, is associated with negative economic, social, and public health outcomes. Here, we present a protocol to quantitatively assess PD utilizing a range of metrics. We describe steps for collecting street views, constructing detection models using the YOLOv8 deep learning model, calculating PD scores, and quantifying changes in PD across streets and cites. This protocol serves as a methodological foundation for assessing PD in different countries and regions. 论文展示
BCL北京城市实验室“中国空间失序” （Spatial Disorder in China）项目涵盖《城市街道空间失序的测度：基于街景图像和深度学习的大规模分析》《利用街景图片研究中国北京市中心城区社区空间失序与急性心肌梗死（AMI）出院患者长期复发风险之间的关系》《使用可穿戴相机测量个人对邻里空间失序的移动性暴露》《空间失序视角下的城市街道空间质量测度》《城市公共失序空间的要素识别、测量、影响评价与空间干预》等研究内容，欢迎探索。
