# Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data
###### Abstract  
The acceleration of urbanization has resulted in a heightened awareness of the impacts of urban environments on residents' emotional states. This present study focuses on the Lixia District of Jinan City. By using urban street view big data and deep learning methods, we undertook a detailed analysis of the impacts of urban color features on residents' emotional perceptions. In particular, a substantial corpus of street scene image data was extracted and processed. This was performed using a deep convolutional neural network (DCNN) and semantic segmentation technology (PSPNet), which enabled the simulation and prediction of the subjective perception of the urban environment by humans. Furthermore, the color complexity and coordination in the street scene were quantified and combined with residents' emotional feedback to carry out a multi-dimensional analysis. The findings revealed that color complexity and coordination were significant elements influencing residents' emotional perceptions. A high color complexity is visually appealing, but can lead to fatigue, discomfort, and boredom; a moderate complexity stimulates vitality and pleasure; high levels of regional harmony and aesthetics can increase perceptions of beauty and security; and low levels of coordination can increase feelings of depression. The environmental characteristics of different areas and differences in the daily activities of residents resulted in regional differences regarding the impacts of color features on emotional perception. This study corroborates the assertion that environmental color coordination has the capacity to enhance residents' emotions, thereby providing an important reference point for urban planning. Planning should be based on the functional characteristics of the region, and color complexity and coordination should be reasonably regulated to optimize the emotional experiences of residents. Differentiated color management enhances urban aesthetics, livability, and residents' happiness and promotes sustainable development. In the future, the influences of color and environmental factors on emotions can be explored in depth, with a view to assist in the formulation of fine urban design. urban streetscape; deep convolutional neural network; emotion perception; color features; random forest +
Footnote †: journal: Medicalare complex entities that intertwine the physical components of the built environment. According to the principles of urban design, streets have a natural 'place' function, which provides residents with the opportunity to conduct activities, socialize, and congregate [3]. The term 'urban environment' is used to describe the landscape of a city, encompassing the physical characteristics and cultural accumulation of the built environment. Color design in urban environmental planning is the study of how color can be applied to enhance the visual appeal of urban environments. In the field of urban environment design, the use of color is a crucial element in determining the overall aesthetic impact of a space [4]. As an indispensable component of the urban environment, urban color can be used to intuitively convey the image of a city, to shape the characteristics of a city, and to affect the psychological feelings of residents and tourists. In the field of urban research, there has been a notable increase in interest surrounding the utilization of deep learning and big data technologies, largely due to their distinctive capabilities and substantial potential. The advent of the big data era has seen the widespread use of web images with spatial location attributes as open data for urban built environment research. The combination of web images and machine learning allows for large-scale quantitative measurements of urban color [5]. It is, therefore, imperative that the spatial quality of urban streets is quantified using techniques such as urban streetscape imagery and deep learning.