Combining Deep Learning and Street View Images for Urban Building Color Research

The color of a cityscape plays a significant role in its atmosphere; however, the traditional city color analysis methods cover a wide range but are not precise enough, requiring field sampling, a lot of manual comparisons, and lacking quantitative analysis of color. With the development of artificial intelligence, deep learning and computer vision technology show great potential in urban environment research. In this document, we focus on “building color” and present a deep learning-based framework that combines geospatial big data with AI technology to extract and analyze urban color information. The framework is composed of two phases: “deep learning” and “quantitative analysis.” In the “deep learning” phase, a deep convolutional neural network (DCNN)-based color extraction model is designed to automatically learn building color information from street view images; in the “quantitative analysis” phase, building color is quantitatively analyzed at the overall and local levels, and a color clustering model is designed to quantitatively display the color relationship to comprehensively understand the current status of urban building color. The research method and results of this paper are one of the effective ways to combine geospatial big data with GeoAI, which is helpful to the collection and analysis of urban color and provides direction for the construction of urban color information management.