A street-view-based method to detect urban growth and decline: A case study of Midtown in Detroit, Michigan, USA.

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.

Drivers' Visual Perception Quantification Using 3D Mobile Sensor Data for Road Safety.

To prevent driver accidents in cities, local governments have established policies to limit city speeds and create child protection zones near schools. However, if the same policy is applied throughout a city, it can be difficult to obtain smooth traffic flows. A driver generally obtains visual information while driving, and this information is directly related to traffic safety. In this study, we propose a novel geometric visual model to measure drivers' visual perception and analyze the corresponding information using the line-of-sight method. Three-dimensional point cloud data are used to analyze on-site three-dimensional elements in a city, such as roadside trees and overpasses, which are normally neglected in urban spatial analyses. To investigate drivers' visual perceptions of roads, we have developed an analytic model of three types of visual perception. By using this proposed method, this study creates a risk-level map according to the driver's visual perception degree in Pangyo, South Korea. With the point cloud data from Pangyo, it is possible to analyze actual urban forms such as roadside trees, building shapes, and overpasses that are normally excluded from spatial analyses that use a reconstructed virtual space.