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

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