Deep Feature Migration for Real-Time Mapping of Urban Street Shading Coverage Index Based on Street-Level Panorama Images

Abstract

Urban street shadows can provide essential information for many applications, such as the assessment and protection of ecology and environment, livability evaluation, etc. In this research, we propose an effective and rapid method to quantify the diurnal and spatial changes of urban street shadows, by taking Beijing city as an example. In the method, we explore a novel way of transferring street characteristics to semantically segment street-level panoramic images of Beijing by using DeepLabv3+. Based on the segmentation results, the shading situation is further estimated by projecting the path of the sun in a day onto the semantically segmented fisheye photos and applying our firstly defined shading coverage index formula. Experimental results show that in several randomly selected sampling regions in Beijing, our method can successfully detect more than 83% of the shading changes compared to the ground truth. The results of this method contribute to the study of urban livability and the evaluation of human life comfort. The quantitative evaluation method of the shading coverage index proposed in this research has certain promotion significance and can be applied to shading-related research in other cities.

Keywords

• shading coverage index
• deep feature migration
• urban street
• deep learning
• panorama images