# Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data
###### Abstract
[19] designed a street view segmentation algorithm with a joint attention mechanism and multi-level feature fusion, which uses a compression and excitation network (SENet) to improve the Panoptic-DeepLab network, aggregates the channel context information, enhances the network to learn the important features, and suppresses the useless features, which effectively improves the accuracy of panoramic segmentation. [PERSON] et al. [20], using Google Street View (GSV) data and combining deep learning algorithms, proposed a method to accurately evaluate the sky visibility index (SVF), tree cover index (TVF), and building shading index (BVF) in street valleys. [PERSON] et al. [21] demonstrated the application of deep learning in urban land use classification, which provides background support for the use of the PSPNet model for street view image processing in this study. [PERSON] et al. [22] devised a network designated as the Feature-Driven Spatial Attention Network (FDSANet), which ingeniously incorporates deep separable convolution and attention mechanisms to achieve the optimal balance between the segmentation accuracy and processing speed of the network model, significantly improving the efficiency and performance of semantic segmentation tasks. [PERSON] [23] proposed a semantic segmentation model improved by DeepLabv3+ for the semantic segmentation task of city streetscapes and conducted experimental attempts on a city-level surveillance quality evaluation system platform, which improved the accuracy of semantic segmentation. [PERSON] [24] used the semantic segmentation technique to accurately extract the target buildings in his study and cleverly introduced the attention mechanism to enhance the discriminative ability of the model, which significantly improved the classification effect and provided a high-quality database for the design of a subsequent building classification model. [PERSON] et al. [5] applied color extraction, computer vision processing techniques, and clustering algorithms for image recognition to establish a city color database and quantify color attributes. [PERSON] [25] used the interpolation and regression algorithm of the MATLAB R2023a software platform in urban color to achieve the automatic filling of the dominant city color map and obtain the ideal color map of the dominant city color. [PERSON] et al. [26] integrated the full convolutional neural network (FCN) and random forest (RF) algorithm, successfully extracted the color characteristics of streetscapes, and constructed a corresponding color quantification index. [PERSON] et al. [27] transformed the pixel information of their detection target into HSV color space, introduced the DBSCAN clustering algorithm in the hue-saturation (H-S) plane, and fitted the precise distribution range of the main color from the scattered data by the Quickhull (fast hull) convex packet algorithm. [PERSON] et al. [28] designed a new image segmentation method by combining Lab (CIELAB) color space and the improved adaptive K-means clustering method in order to achieve the adaptive segmentation of different images. In light of the aforementioned findings, the present study represents an extension of previous research, employing deep learning and street view big data to investigate the influences of urban color features on residents' subjective emotional perceptions.