KAIST Urban Design Lab, Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
PLoS One. 2022 Feb 8;17(2):e0263775. doi: 10.1371/journal.pone.0263775. eCollection 2022.
Urban growth and decline occur every year and show changes in urban areas. Although various approaches to detect urban changes have been developed, they mainly use large-scale satellite imagery and socioeconomic factors in urban areas, which provides an overview of urban changes. However, since people explore places and notice changes daily at the street level, it would be useful to develop a method to identify urban changes at the street level and demonstrate whether urban growth or decline occurs there. Thus, this study seeks to use street-level panoramic images from Google Street View to identify urban changes and to develop a new way to evaluate the growth and decline of an urban area. After collecting Google Street View images year by year, we trained and developed a deep-learning model of an object detection process using the open-source software TensorFlow. By scoring objects and changes detected on a street from year to year, a map of urban growth and decline was generated for Midtown in Detroit, Michigan, USA. By comparing socioeconomic changes and the situations of objects and changes in Midtown, the proposed method is shown to be helpful for analyzing urban growth and decline by using year-by-year street view images.
城市的增长和衰退每年都在发生，并呈现出城市地区的变化。虽然已经开发出了各种检测城市变化的方法，但它们主要使用大规模的卫星图像和城市地区的社会经济因素，提供了城市变化的概述。然而，由于人们每天都在街道层面上探索地点并注意到变化，因此开发一种方法来识别街道层面的城市变化并展示那里是否发生了城市增长或衰退将是有用的。因此，本研究旨在使用来自 Google Street View 的街景全景图像来识别城市变化，并开发一种新的方法来评估城市区域的增长和衰退。在逐年收集 Google Street View 图像之后，我们使用开源软件 TensorFlow 训练和开发了一个对象检测过程的深度学习模型。通过对街道上年复一年检测到的对象和变化进行评分，为美国密歇根州底特律市的中城区生成了一张城市增长和衰退的地图。通过比较社会经济变化以及中城区的物体和变化情况，该方法被证明有助于通过逐年的街景图像分析城市的增长和衰退。
