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

Jiaxin Zhang,Tomohiro Fukuda,Nobuyoshi Yabuki

doi:10.3390/ijgi10080551

Jiaxin Zhang, Tomohiro Fukuda + Show 1 more

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https://doi.org/10.3390/ijgi10080551

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Abstract

Precise measuring of urban façade color is necessary for urban color planning. The existing manual methods of measuring building façade 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 façade 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 façades and conducted the color calibration of the photos for pre-processing the collected street view images. Then, we proposed a color chart-based façade 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 façade color measurement and building function classification. Finally, we applied our approach to generate façade 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 façade color and building function across city-scale areas in a cost-efficient way, contributing to data-driven decision making for urban analytics and planning.

Highlights

In the last several decades, empirical observations and scientific studies have proven that human-environment reaction in an urban environment is primarily based on the sensory perception of its color [1]
Compared with methods through field survey measurements, this paper developed an automatic method for façade dominant color measurement with building functional classification using state-of-the-art deep learning models and extensive-coverage street view images, significantly improving the efficiency of city-scale data analysis
Since the accuracy of façade color is strongly influenced by the segmentation result, it is vital to verify the generalizability of the pre-trained model to the street view images of our study areas

Summary

Introduction

In the last several decades, empirical observations and scientific studies have proven that human-environment reaction in an urban environment is primarily based on the sensory perception of its color [1]. City managers have attached great importance to urban color and issued a series of color planning rules in urban development [2]. The urban function is one of the critical factors that designers need to consider when implementing urban color planning [2]. Many urban planners have suggested that buildings for specific functions need to comply with a spectrum of colors [4]. The acceleration of urbanization poses troubles for urban color planning [5]. Some urban designers do not consider the influence of the surrounding color of the environment when developing new buildings or renovating existing ones [6]. Many emerging architectural styles are becoming similar in fast-growing cities, resulting in the loss of urban color identity [7]

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