Using Street-level Images and Deep Learning for Urban La ndscape STUDIES

Abstract

Streets are a focal point of human activities and a major interface of the social interaction between urban dwellers and urban built environment. A better understanding of the urban landscapes along streets is thus important in urban studies. The increasing availability of street-level images provides new opportunities for urban landscape studies to study and analyze streetscapes at a fine level and from a different perspective. In this study, we presented an application of a recently developed Deep Convolutional Neural Network on landscape analysis based on street-level images. Different urban features were identified from street-level images accurately using a trained Deep Convolutional Neural Network model. Based on the image segmentation results, we further measured the spatial distribution of the street greenery and quantitatively analyzed the openness of street canyons in Cambridge, Massachusetts. The proposed combination of Artificial Intelligence and the massively collected street-level images provides a new sight for urban landscape studies for cities around the world.

Keywords

Convolutional Neural Network / Urban Street / Artificial Intelligence / Machine Learning / Image Segmentation

RIGHTS & PERMISSIONS

2018 Higher Education Press