Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km² rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

Precise urban façade color is the foundation of urban color planning. Nevertheless, existing research on urban colors usually relies on manual sampling due to technical limitations, which brings challenges for evaluating urban façade color with the co-existence of city-scale and fine-grained resolution. In this study, we propose a deep learning-based approach for mapping the urban façade color using street-view imagery. The dominant color of the urban façade (DCUF) is adopted as an indicator to describe the urban façade color. A case study in Shenzhen was conducted to measure the urban façade color using Baidu Street View (BSV) panoramas, with city-scale mapping of the urban façade color in both irregular geographical units and regular grids. Shenzhen’s urban façade color has a gray tone with low chroma. The results demonstrate that the proposed method has a high level of accuracy for the extraction of the urban façade color. In short, this study contributes to the development of urban color planning by efficiently analyzing the urban façade color with higher levels of validity across city-scale areas. Insights into the mapping of the urban façade color from the humanistic perspective could facilitate higher quality urban space planning and design.

Living convenience, as a perceptual quality of life, is gradually playing an increasingly important role in the context of seeking livable cities. A high degree of living convenience positively affects urban vitality, livability, and daily physical activities. However, it is hard to achieve a quantitative measurement of this intangible, subjective issue. This study presents a data-informed analytical approach to measuring the human-scale living convenience using multi-sourced urban data and geodesign techniques. Firstly, according to classical theories, living convenience is translated as the co-presentation of accessed number and diversity of urban facilities. Based on that, this study applies multi-sourced urban data, including points of interest (PoIs), buildings, and street networks, to compute the living convenience of each building in the 15 min community–life circle. Through the geoprocessing tools developed by ArcGIS API for Python (ArcPy), the living convenience of millions of buildings in an entire city can be computed efficiently. Kaifeng City from Henan Province, China, is selected as the case study, and the verification from local experts in urbanism shows high accuracy. The capacity to measure intangible perception exhibits the potential for this analytical approach in urban planning practices. Several explorations have been conducted in this direction, including analyzing the spatial heterogeneity in Kaifeng City and planning decision support for bus station arrangement. In short, this study contributes to the development of human-centered planning by providing continuous measurements of an ‘unmeasurable’ quality across large-scale areas. Insights into the perceptual-based quality and detailed mapping of living conveniences in buildings can assist in efficient planning strategies toward more livable and sustainable urbanism.

Building color and harmoniousness have been regarded as critical issues in planning historic districts. Harmoniousness of building façade colors (HBFC) is an indicator to evaluate the quality of the built environment, which can be perceived but is difficult to measure quantitatively. In addition, alleviating the impact of shadows in street-view images (SVIs) to assess building façade color is another research gap that is difficult to address. This paper proposes an efficient approach for evaluating HBFC on a large-scale using SVIs and a deep learning algorithm. Specifically, a shadow processing method was developed, and transfer learning was integrated into the harmoniousness evaluation process. The historical district of Guangzhou, China, was selected as a case study area. This study contributes to the development of human-centered planning and design by providing continuous measurements of “unmeasurable” quality across large-scale areas. Meanwhile, insights into building façade color and its harmoniousness can assist with accurate design guidance, which is important for historic districts.

Pavement markings could wear out before their expected service life expires, causing traffic safety hazards. However, assessing pavement-marking conditions at the city scale was a great challenge in previous studies. In this article, we advance the method of detecting and evaluating pavement-marking defects at the city scale with Baidu Street View (BSV) images, using a case study in Nanjing. Specifically, we employ inverse perspective mapping (IPM) and a deep learning-based approach to pavement-marking extraction to make efficient use of street-view imageries. In addition, we propose an evaluation system to assess three types of pavement-marking defects, with quantitative and qualitative results provided for each image. Factors causing pavement-marking defects are discussed by mapping the spatial distribution of pavement-marking defects at the city scale. Our proposed methods are conducive to pavement-marking repair operations. Beyond this, this article can contribute to smart urbanism development by creating a new road maintenance solution and ensuring the large-scale realization of intelligent decision-making in urban infrastructure management.