1. Introduction
In the contemporary quest for superior quality development, the generation of refined urban public spaces has emerged as a subject of paramount interest for urban planners and administrators. Serving as public arenas that foster physical activity and routine social engagement among city dwellers [
1], the caliber of urban streets (encompassing both tangible spatial quality and intangible psychological perception) constitutes a vital benchmark for gauging a city’s habitability and its commitment to human-centered principles [
2]. Interdisciplinary investigations have corroborated the positive influence of exceptional streets on residents’ well-being, bolstering morale whilst effectively diminishing the prevalence of assorted maladies [
3,
4,
5,
6]. Gaining a genuine and precise understanding of the current quality of street spaces and residents’ emotional perception towards urban areas is of paramount importance to government officials and urban planners. This knowledge dictates how they should optimize and enhance the environment, thereby attracting a greater influx of talents, investors, and enterprises. Such attraction, in turn, serves as a robust catalyst for propelling urban development [
7]. Streets, being the carriers of numerous urban functions, garnered attention from the academic sector quite early on. In the 1960s, the burgeoning phenomena of urban space segregation, quality decline, and loss of vitality became prevalent, leading a group of pioneering individuals in urban research, including Jane Jacobs [
8] and Henri Lefebvre, to begin exploring the quality of street spaces and their impact on society and economy. Subsequently, the dimensions of research concerning street spaces diversified. Allan Jacobs conducted an extensive analysis of hundreds of streets located around the world from various physical spatial perspectives including plan, section, scale, street D/H ratio, and the degree of refinement in street furniture. Through this meticulous examination, he summarized and identified the physical attributes inherent to great streets [
9].
The methodologies for assessing street quality have continually evolved with the advancement of science and technology. Early street perception research relied on random sampling [
10,
11,
12], surveys, or field investigations [
13]. McGinn and his colleagues conducted telephone interviews to gather perceptions of the built environment from diverse groups [
14], and Sallis et al. conducted an evaluation of travel route spatial environments via 43 questionnaires [
12]. Nonetheless, these modest-scale empirical investigations, rooted in local spaces, lack robust universality, and the protracted process data of acquisition and challenges in quantification render them ill-suited to support more rigorous, large-scale research on street quality. The maturation of scientific knowledge and the widespread adoption of computer technology have engendered a novel data landscape, paving the way for meticulous street studies. The procurement of high-resolution streetscape imagery and the alleviation of quantification difficulties have spurred a growing body of scholars to undertake the quantitative depiction of urban built space environmental characteristics and quality using streetscape images as a foundation [
15,
16,
17,
18]. The advancement of machine learning algorithms furnishes technical backing for more fine-grained inquiries. Deep learning algorithms like SegNet and ResNet, in tandem with deep convolutional neural networks and support vector machines, facilitate the efficient deep processing of streetscape images. Multiple elemental features within images, including sky, road, buildings, and landscape, can be effectively identified [
19,
20,
21]. With streetscape data serving as the blueprint, researchers such as Li Xiaojian have validated the significance of streetscape data in quantifying and mapping urban environmental features [
22], prompting extensive inquiries into green view indices and street sequences. Scholars including Yang Zhuo [
18], Long Ying [
19,
20,
21], Ye Yu [
20], and Zhao Qing [
23] employed these data for comprehensive quantitative examinations of street spatial quality.
The perceptual research highlighted above primarily focuses on qualitatively and quantitatively describing the objective spatial quality from a user’s physiological standpoint. With the advent of human-centric ideologies, studies exploring users’ subjective psychological perceptions of space have gradually emerged. The notable psychological experiment by experimental psychologist Treichler indicates that humans obtain 83% of perceptual information through vision, compared to other sensory modalities such as hearing and touch [
24]. This substantiates that users’ perception of urban street spaces predominantly hinges on visual cues rather than auditory, olfactory, or tactile cues. The external features of space, encompassing its shape, color, and geometric structure, significantly impact users’ psychological perception.
In his book
The Luminous Ground [
25], architect Alexander mentions that the foundation of every act of construction or creation should be rooted in genuine conscious emotions and personal experiences. Under the human-centered developmental orientation in urban spaces, it is imperative to have designs based on the authentic emotional perceptions of the users. In order to gauge the impact of the urban visual environment on the emotional states of perceivers, the MIT Media Lab, in collaboration with machine learning, initiated a project named Place Pulse, which employed numerous streetscape images to discern and evaluate urban spatial quality across six dimensions, constructing psychological perception maps of urban residents’ street spaces [
26]. A multitude of urban perception studies have ensued [
27,
28], with researchers such as Liu Liu, Zhang Fan [
29], and others developing a streetscape image perception scoring model based on deep convolutional neural networks, analyzing the visual elements representing the characteristics of Beijing and Shanghai from the aforementioned six dimensions. Based on a human–machine adversarial model, Wang Lei and his colleagues [
30] delineated the urban perception map of the Binjiang District of Hangzhou City and conducted an analysis of the spatial elements affecting residents’ perceptions based on emotion scoring. Moreover, there are researchers concentrated on aspects such as street vitality [
31], safety [
32,
33], happiness [
34], and even the link between streetscape quality and geriatric depression from a medical standpoint [
35]. Ann Sussman and colleagues have bridged biology with architecture and environmental design, innovatively employing eye-tracking devices and visual attention simulation software through a series of experiments. By capturing users’ authentic experiences, they have unveiled the factors that render streets most conducive to walking, thereby serving human health and well-being [
36,
37,
38,
39,
40,
41,
42,
43,
44,
45].
In existing research, the perception of residents regarding the visual aesthetic quality of urban spaces and its impact on their emotions is seldom discussed. Recognized as a complex structure that operates across various dimensions [
46], color’s effect on human emotions is a fact well acknowledged within academia, thereby sparking discussions across multiple disciplines. The choice of color in advertising and promotional campaign posters can influence consumers’ perception of products or services, and might even prove more effective than marketing slogans in generating sales [
47]. In the medical realm, mounting evidence reveals that plants of different colors can have varying impacts on emotions, psychological and physiological well-being [
48,
49]. Color psychology also indicates that color can exert varying degrees of influence on human psychological health and emotions, which in turn relate to the quality of residents’ lives [
50,
51] and levels of physical and mental health [
52,
53], while also affecting the degree to which users favor a certain location. People aspire to be in environments that enhance work efficiency, and contribute to physiological and psychological well-being, and are inclined to visit spaces that can significantly improve their emotional states. Evans’s study also confirms that color is a key solution for enhancing urban experiences and enjoyment [
54].
In human visual perception, color is perceived foremost, with individuals typically noticing the color of a building prior to its architectural form. Image clarity and quality have minimal influence on color, and even at lower resolutions, people can correctly perceive colors and the object outlines they form. Thus, color, being an indispensable component of aesthetics, occupies a significant position in the realm of spatial design. Presently, there exists an abundance of studies concerning urban color; however, scholarly attention appears to be more oriented towards the perception of color within indoor spaces and related issues [
55,
56,
57]. At the urban scale, pertinent research predominantly centers around architectural color, urban color planning, and engages in both qualitative and quantitative inquiries into urban imagery. In Chapter 7 of Christopher Alexander’s book—
The Luminous Ground—he gives detailed instructions on how to use the most appropriate colors in architectural creation [
25]. Scholars such as Ye Yu [
58], Ding Meichen [
59], Jiang Bo [
60], Zhong Teng [
61], and others have harnessed streetscape images and computer recognition technology to execute large-scale quantitative analyses and evaluations of urban and architectural colors. Zhu Xiaoyu and her colleagues [
62] have fine-tuned the investigation of color luminosity and chromaticity, drawing upon the extraction of buildings’ dominant colors, thereby offering a constructive reference for urban color planning. Danaci and colleagues embarked from the perspective of color perception, analyzing the attitude changes of subjects towards three major streets in Antalya before and after painting. Consequently, they discerned the impact of the color of architectural facades on urban aesthetics [
63].
Although color is an inherent feature of every object’s surface, there remains a gap in empirical studies investigating the impact of color in urban external spaces on residents’ emotional perceptions, with little deep exploration into the dimensions of color composition. Although some studies have ventured into employing street-level imagery data to quantitatively evaluate the relationship between urban street environments and residents’ psychological well-being in major urban regions, the majority of these case studies are based in cities across North America and Europe, with a noticeable paucity of such research conducted in Asian cities.
The existing body of research has amply demonstrated the feasibility of urban spatial perception prediction based on subjective human perception and machine learning. Hence, utilizing street-view imagery along with the CEP-KASS framework outlined in this document, we strive to establish a connection between color and emotion, evaluating the relationship between the street environments within the core urban area of Xuzhou and residents’ emotions from the angle of color perception. Our research homes in on the following questions: How can one accurately delineate the predominant colors of urban streets across a broad scope? Do the environmental traits and compositional elements of various streets, together with their respective colors, impact residents’ emotional perception of the space? What sort of influence do the color characteristics of urban spaces exert on residents’ emotions? Additionally, the study identifies priority areas for urban color planning and renewal based on multi-dimensional data (including spatial syntax, POI, and mobile signaling data), an imperative for second-tier cities like Xuzhou. The findings herein could significantly guide the progression of urban renewal projects and the distribution of related construction funds. This investigation extends the current body of research, filling a void concerning residents’ color perception within urban settings and, from the perspective of color planning, holds practical significance for urban planners aimed at fostering urban environments conducive to enhancing mental well-being. This perspective enables planners to better sculpt urban spaces from the users’ viewpoint.
4. Results and Discussion
4.1. Color and Visual Elements Correlation Analysis
In the CEP-KASS framework, the SegNet model, commonly used for street scene semantic segmentation, was applied on the MIT ADE20K dataset to semantically segment the concatenated BSVI. This provided visual element data, listing the top 12 visual elements by area percentage in BSVI, as seen in
Table 7.
Colors were extracted using K-Means and then categorized as per
Table 1. A Pearson correlation was used to study the relationship between different colors and visual elements, thereby elucidating the relationships between various colors and visual elements within the urban streets of the study area. The results of which are shown in
Table 8 with significance annotations. Given the diversity and complexity of colors constituting the visual elements, the findings only indicate a correlation between specific colors and some visual elements, but they aid urban planners and designers in understanding the primary color compositions of visual elements within the street environment. Based on correlation values: below 0.3 is considered almost no correlation, 0.3–0.5 is weak, 0.5–0.7 is moderate, and above 0.7 is strong. The positivity or negativity of the correlation is indicated by the positive or negative value, with different levels of significance marked by * in the table. The study unveils, at various levels of confidence, the correlation within the street environment of the inner ring in Xuzhou. We note that Gray_S1_V2 strongly correlates with walls and pavements, Black_S3_V1 moderately correlates with roads, pavements, and grounds, and Green_S2_V2 has a strong correlation with trees and plants.
4.2. Color and Perception Score Correlation Analysis
CEP-KASS correlated color data with scores from five perception dimensions using Pearson correlation, visualizing results in a table. Red indicates positive correlation, blue indicates negative, with color intensity representing correlation strength. After marking significance, the results are presented in
Table 9. Planners and designers can refer to
Table 8 for guidance on how to control specific colors and their associated attributes in the color renewal and planning of urban street environments. By modulating these color attributes, it is possible to regulate perceptions related to residents’ emotions, thereby ameliorating the emotional variations residents experience during their daily commutes due to the influence of urban colors.
The table reveals relationships between the street color environment in the inner circle of Xuzhou and the five perception scores:
In terms of specific color selection:
- (1)
Black_s3_v1 strongly and positively correlates with the beautiful score and safe score but negatively correlates with the wealthy score, all significant at
p < 0.005. In
Table 7, roads, pavements, and grounds are primarily associated with Black_s3_v1.
- (2)
Red_s1_v3, orange_s2_v2, and blue_s1_v2 all strongly and positively correlate with the interesting score, whereas green_s1_v1 negatively correlates. This suggests that vibrant colors with medium-to-low saturation can capture attention, having a strong correlation with the interesting score. Existing studies have also demonstrated that colors with higher saturation may have a negative impact on emotions.
- (3)
As for green, Green_s1_v1 and green_s1_v2 show a strong positive correlation with the lively score in visual perception, with green_s1_v1 also positively correlating with the wealthy score. In
Table 7, trees, grass, and plants are mainly associated with these green shades. These conclusions corroborate with previous studies. But the strong negative correlation between green_s1_v1 and the interesting score suggests that, for the color green, excessively low saturation and brightness levels may diminish its appeal to the residents.
Within the specific attributes of color, the study also discovered that vivid colors with medium-low saturation and brightness negatively correlate with the beautiful score.
4.3. Division of Streets Based on Perception Scores and Perception Frequency
The study of urban street environment color perception can provide urban planners and designers with design and planning support from the perspective of resident perception, identifying streets worth referencing in urban renewal and color transformation, and those that need to be prioritized for remodeling. To achieve this goal, it is necessary to categorize the streets, assuming what the color perception score of each street and the perception frequency is. Through the perception scores, the top 15% of streets are defined as threshold, and the bottom 15% of streets are defined as threshold, based on multiple data to determine the perception frequency of different streets. According to Equation (7), streets are divided into five types: ① High Score High Frequency Perception (
HSHF); ② High Score Low Frequency Perception (
HSLF); ③ Low Score High Frequency Perception (
LSHF); ④ Low Score Low Frequency Perception (
LSLF); ⑤ Others.
HSHF (High Score High Frequency Perception) streets can serve as good references and models for the color environment of urban streets, requiring no re-planning in subsequent urban color transformations. We suggest designating these streets as urban color nodes and reference samples for the color transformation of other streets.
For HSLF (High Score Low Frequency Perception) streets, urban planners can compare them with HSHF streets to explore the differences in color environment between these two types of streets. However, due to the significant difference in perception frequency, certain factors such as high accessibility and points of interest (POIs) that can attract pedestrian traffic should be considered. If necessary, color adjustments can be made to enhance their appeal and guide pedestrians towards these streets. Any color transformation in this regard should primarily aim to increase street appeal and the frequency of pedestrian perception.
LSHF (Low Score High Frequency Perception) streets are frequently perceived during daily commutes, but the color perception score experience by pedestrians is subpar, thus having a high priority in color transformation and regulation. Optimizing the color environment of these streets can better exploit their functionality. Improvements can be made by referencing the characteristics of high-score streets.
LSLF (Low Score Low Frequency Perception) streets score low in color perception and also have a lower perception frequency, possibly due to their remote location or weaker functionality, which results in fewer visits by residents and pedestrians. When considering color transformation and regulation, these streets should be given lower priority.
Streets categorized as ‘Others’ have moderate scores and perception frequency in terms of color perception. Planners and designers should conduct detailed onsite inspections and analyses to determine the necessity of transformation and should reference the characteristics and experiences of HSHF streets in their decision-making process.
Figure 6 displays the division of
HSHF (High Score High Frequency Perception) and
LSHF (Low Score High Frequency Perception) streets under the poverty–wealthy dimension. Following this logic and visualizing the color perception and perception frequency for the remaining four dimensions, we obtained
Figure 7. The study discovered that within the inner ring of Xuzhou, streets with high perception frequency and high accessibility tend to score higher in the danger–safe dimension of color environment perception. Previous research also confirms that street accessibility often plays a critical role in creating places and enhancing the quality of life, providing a set of urban street space quality measurement standards based on accessibility (Ye et al., 2019).
Additionally, streets scoring higher in color environment perception across the five dimensions often have urban nodes nearby. This conclusion is more evident in the overlay analysis of color environment perception and perception frequency in the ugly–beautiful dimension, corroborating that the aesthetic quality of street spaces around urban nodes is higher. Overlaying the depressing–lively score, boring–interesting score, and poverty–wealthy score dimensions with high-frequency perception streets revealed a continuous distribution of high-score points. Among these, the boring–interesting score dimension demonstrates a clustered distribution along the riverside areas. It is advisable for urban planners to prioritize referencing the color environment of these streets when shaping the city’s image and planning for color transformations.
Low perception scores in street environments overlap considerably in the ugly–beautiful score and depressing–lively score dimensions, with most overlaps occurring in the city center and western city regions. These areas largely comprise older urban districts with narrow streets, necessitating color transformations and adjustments to enhance the commuting experience for residents.
4.4. Street Color Environment Analysis
4.4.1. Analysis of H, S, V Relationships
Scatter plots illustrating the relationships among H, S, V (hue, saturation, value) were constructed for the colors of the streets in the study area prior to converting them to the H, S, V range in OpenCV, resulting in
Figure 8. In the V-H plot, there is an absence of data in the high-value range for the yellow-green-cyan colors. Cross-referencing with
Table 7 reveals that the primary visual elements providing such colors in the city are vegetation, which finds it challenging to achieve high values in color representation. In the S-V plot, high saturation colors predominantly occupy the low-value range, and in the S-H plot, they are primarily situated in the red range. This demonstrates that the high saturation colors in street scenes are chiefly composed of reds with high saturation and low value.
As saturation diminishes, the primary composition of medium saturation colors transitions to orange, where, as analyzed in
Table 8, medium-saturation and medium-value orange holds the strongest positive correlation with the boring–interesting score. The colors manifesting as deep red, brownish-red, brown, and even brownish deep wooden hues in the street scenes of the study area are mainly derived from red soil, tree trunks, traditional Chinese wooden structures, and buildings adorned with traditional wooden color themes. The “Historical Records” document that the soil in Xuzhou is red, sticky, and fertile, and current geological studies corroborate the presence of a substantial amount of red soil in Xuzhou. This suggests that the deep wooden hues of traditional architecture and the local red soil colors continue to pique visual interest. Nonetheless, the medium-saturation and medium-value orange, while augmenting the perception on the interesting score dimension, causes a decrement in perception scores on the lively score and beautiful score dimensions. This might be attributed to the fact that modern constructions employing these colors do not utilize mud and wood materials, but rather opt for stone veneer or concrete plaster exterior walls. The extensive color facades adversely affect the lively score and beautiful score dimensions.
As saturation further recedes, medium-saturation colors now also encompass cyan, blue, and purple. These three colors, predominantly reflected in the glass and curtain walls of modern edifices in street scenes, do not exhibit any correlation with any perceptual dimensions in the correlation analysis. During the color renovation of urban street environments, planners can ascertain the relationship between colors and varying perceptual dimensions through the analyses in
Table 8 and
Figure 8, based on the perceptual dimensions necessitating optimization, thereby ameliorating color perception experience through color modulation.
4.4.2. Analysis of Street Color Attributes
The research area was divided into grids of 300 m × 300 m, and then the color with the highest frequency of occurrence in each grid was calculated to fill the grid. After extracting the corresponding brightness and saturation of the color, it was visualized on the map as shown in
Figure 9. From the distribution of brightness, it can be seen that the brightness is lower in the city center, the western urban area, and near the main roads, which is related to the large proportion of asphalt roads in the visual field in these places. In the saturation distribution map, there are exposed red soils along the east coast of Yunlong Lake and at Jiuli Mountain, whereas other places with high saturation have buildings with deep woody colors, such as the Qianlong Palace, being an ancient building, and the Wenchang Campus of China’s University of Mining and Technology, being a modern building with a large area decorated in these colors.
Figure 10 shows a hotspot analysis of the distribution of brightness and saturation in urban spaces to intuitively display the areas of extreme value aggregation of saturation and brightness within the research area. Based on this, the relationship between the areas of extreme value aggregation of brightness, saturation, and emotional perception in the city is explored.
For the areas of interest, in conjunction with perceptual scores,
Table 8 can be used to quickly determine the kind of color regulation required within each grid area. Furthermore, by considering the frequency of perception, the priority levels for color transformation and regulation can be established, and specific regulation schemes can be determined based on the correlation between color and perception.
Figure 11 exemplifies this with an analysis of hot and cold spots in terms of brightness, where streets with the highest 15% and lowest 15% perceptual scores have significant overlap with the extreme value clusters, with the exception of the poverty–wealthy dimension. Specifically, points with a higher interesting score align to some extent with areas of higher brightness, suggesting that a continuous distribution of high-brightness colors can engage the interest of residents; points with a lower beautiful score and lively score overlap with areas of lower value, indicating that regulating value can improve color perception in these dimensions; points with a higher safe score overlap with areas of lower value, whereas points with lower scores overlap with areas of higher value, proving an inverse relationship between value and the perception of safety. Hence, by inversely regulating value, the perceptual experience in the safety dimension of some specific urban external spaces can be improved.
4.5. Limitations and Future Research
Although this study explores color perception in urban street environments, there are some shortcomings that warrant further discussion in future work. Firstly, this study selected a single research area and employed the proposed CEP-KASS framework to investigate color perception in urban street environments. In reality, as long as there is a sufficient amount of street-view image data and the city reaches a certain scale, this framework can be applied to different cities, and by comparing multiple cities of the same level, more universal color rules can be elucidated. Additionally, this study did not provide multi-level explanations for the formation of the perceptual results of street environment colors. Future research could consider incorporating eye-tracking and visual attention simulation experiments, capturing participants’ eye movement paths precisely when observing urban scenes, and predicting and simulating people’s attention distribution while viewing urban scenes. This would reveal which spaces within the urban scene are noticed first when color environments impact perception, aiding researchers in the fields of urban science and anthropology to more intuitively understand the formation of perceptual outcomes caused by which visual elements and corresponding colors. Moving forward, we will consider more factors, increase the sample of cities, and combine some intuitive experiments to discuss urban street color planning.
5. Conclusions
The color environment of urban streets plays a significant role in optimizing residents’ daily travel perception experience. We should rationally plan the urban street color environment to enhance residents’ travel color perception experience and build a city color environment centered on human perception.
Previous research rarely studied street environment color from the perspective of urban color environment perception. To fill this research gap, we proposed a methodological framework named CEP-KASS and validated it using an area within the third ring of Xuzhou as the study subject. First, visual elements and color data of streets were extracted from BSVI. The model we used for segmentation was a fine-tuned SegNet model, achieving 42.14 in mIoU, 80.13% in pixel accuracy, and 61.44 in overall score.
Then, after obtaining the color environment perception score data through the adversarial model, we used the support vector machine to predict the perception scores of all streets. Its MSE and R square reached 0.2188 and 0.8327, respectively, showing good predictive results. Finally, by integrating road accessibility, population heatmaps, and POI distribution, we determined the perception frequency of streets. Combining this with color environment perception scores, we categorized streets into five types: HSHF, HSLF, LSHF, LSLF, and Others. Urban planners are advised to prioritize referencing HSHF streets and renovating LSHF streets when shaping the city’s image and planning its colors.
Furthermore, the study classified the street color environment extracted from BSVI and studied the relationship between color and visual elements as well as color and its related attributes with perception scores through Pearson correlation analysis and applied the conclusions to street environment color analysis. For instance, medium saturation and luminance of the color orange, while enhancing the perception score in the interesting score dimension also affected perception scores in the lively score and beautiful score dimensions. Therefore, color regulation can be employed to optimize residents’ emotions. After analyzing the perception scores of streets, combined with the classification rules of streets, it is feasible to determine whether color regulation is needed, and relevant color control suggestions can be provided based on the correlation study of color with visual elements and perception scores.
In summary, we selected the often-overlooked color aspect in urban design, applied the CEP-KASS framework to conduct research on color with visual elements, and perception scores, providing theoretical support for urban color planning from a resident-centric perspective on color perception. The five classification criteria for streets offer a reference for prioritizing color renovation, and the hot and cold spot analysis of street color elements helps ascertain how luminance and saturation affect residents’ perceptions. This contributes to the practical application of color perception conclusions and provides scientific color renovation suggestions based on the actual situations of streets, optimizing urban residents’ spatial perception and emotional experience within a city.