Vallebueno Andrea, Lee Yong Suk
Regulation Evaluation and Governance Lab, Stanford University, Stanford, 94350, USA.
Keough School of Global Affairs, University of Notre Dame, Notre Dame, 46556, USA.
Sci Rep. 2023 Oct 12;13(1):17316. doi: 10.1038/s41598-023-44551-3.
The quality of the urban environment is crucial for societal well-being. Yet, measuring and tracking the quality of urban environment, their evolution, and spatial disparities is difficult due to the amount of on-the-ground data needed to capture these patterns. The growing availability of street view images presents new prospects in identifying urban features. However, the reliability and consistency of these methods across different locations and time remains largely unexplored. We aim to develop a comprehensive index of urban quality and change at the street segment level using Google Street View (GSV) imagery. We focus on eight object classes that indicate urban decay or contribute to an unsightly urban space, such as potholes, graffiti, garbage, tents, barred or broken windows, discolored or dilapidated façades, weeds, and utility markings. We train an object detection model on a dataset of images from different cities and assess the performance of these urban indices. We evaluate the effectiveness of this method in various urban contexts over time and discuss its potential for urban planning and public policy. We demonstrate the use of these indices in three applications: the Tenderloin in San Francisco, the Doctores and Historic Center neighborhoods in Mexico City, and South Bend, Indiana.
城市环境质量对社会福祉至关重要。然而，由于需要大量实地数据来捕捉这些模式，测量和跟踪城市环境质量、其演变以及空间差异颇具难度。街景图像可用性的不断提高为识别城市特征带来了新的前景。然而，这些方法在不同地点和时间的可靠性和一致性在很大程度上仍未得到探索。我们旨在利用谷歌街景（GSV）图像开发一个街道段层面的城市质量与变化综合指数。我们关注八个表明城市衰败或导致城市空间难看的对象类别，例如坑洼、涂鸦、垃圾、帐篷、有栅栏或破碎的窗户、变色或破旧的外立面、杂草以及公用设施标记。我们在来自不同城市的图像数据集上训练一个目标检测模型，并评估这些城市指数的性能。我们随时间评估该方法在各种城市环境中的有效性，并讨论其在城市规划和公共政策方面的潜力。我们展示了这些指数在三个应用中的使用：旧金山的田德隆区、墨西哥城的多克托雷斯和历史中心社区以及印第安纳州的南本德。
