Development of a City-Scale Approach for Facade Color Measurement with Building Functional Classification Using Deep Learning and Street View Images

Precise measuring of urban facade color is necessary for urban color planning. The existing manual methods of measuring building facade color are limited by time and labor costs and hardly carried out on a city scale. These methods also make it challenging to identify the role of the building function in controlling and guiding urban color planning. This paper explores a city-scale approach to facade color measurement with building functional classification using state-of-the-art deep learning techniques and street view images. Firstly, we used semantic segmentation to extract building facades and conducted the color calibration of the photos for pre-processing the collected street view images. Then, we proposed a color chart-based facade color measurement method and a multi-label deep learning-based building classification method. Next, the field survey data were used as the ground truth to verify the accuracy of the facade color measurement and building function classification. Finally, we applied our approach to generate facade color distribution maps with the building classification for three metropolises in China, and the results proved the transferability and effectiveness of the scheme. The proposed approach can provide city managers with an overall perception of urban facade color and building function across city-scale areas in a cost-efficient way, contributing to data-driven decision making for urban analytics and planning.

Automated summary

This paper explores a city-scale approach to facade color measurement with building functional classification using state-of-the-art deep learning techniques and street view images. By establishing facade color measurement and building classification methods, the accuracy of measurement can be effectively improved, providing city managers with an overall perception of urban color and building function.