# City Architectural Color Recognition Based on Deep Learning and Pattern Recognition
## 2 Background and Related Work
[PERSON] and [PERSON] implemented a method of extraction of crop neural networks using the color characteristics of the color histogram feature extraction algorithm [23]. Deep learning, as evidenced by the references, primarily focuses on its versatile applications across different domains and optimizing diverse datasets [24; 25]. In the realm of architecture, the predominant emphasis lies in extracting building outlines from satellite imagery to enable easy demarcation of urban regions. However, there has been comparably less emphasis on leveraging deep learning techniques to explore and enhance urban architectural aesthetics. Within the realm of smart city planning, based on deep learning, convolutional neural networks enable the evaluation of the appearance of building texture, style, and color through image or video data, facilitating real-time monitoring and intelligent planning of smart cities. Additionally, in the age of iterations, it ensures the functional and visual effectiveness of urban architecture, enhancing the city's integration and promoting its distinctive characteristics while upgrading and updating the related intelligent computing models for city perception. Moreover, monitoring building patterns provides valuable guidance for subsequent planning and design improvements in smart city development, as well as predictive insights for personalized city development. Applying the K-means clustering algorithm in smart city planning enables the extraction of a specific number of colors from urban buildings, allowing for the analysis and calculation of their color ratios. In smart city planning, the development of digitalization is essential, and the intelligent visual representation of the city also plays a vital role. Color serves as the language of a city, reflecting its cultural traditions and spirit. Well-chosen urban colors contribute to the city's development and communicate its character. Collecting urban architectural colors facilitates the creation of an accurate portrayal of the city in the metaverse, forming a comprehensive color scheme based on specific color data. [PERSON] et al. classified and analyzed traffic driver's behavior using the K-means algorithm [26]. [PERSON] et al. enhanced and upgraded the grayscale model of each clustering class based on the K-means algorithm [27]. [PERSON] et al. segmented a corn blade image based on the K-means algorithm to reduce the impact and improve accuracy using the HSV color space's H component [28]. In the realm of remote sensing imagery, [PERSON] utilized K-means and PHQ to balance image quality with expedited execution time [29]. By employing image-recognition techniques based on convolutional neural networks and K-means clustering algorithms, the proposed approach leverages the deep learning of architectural features to automatically identify urban buildings and extract their colors and background colors. This establishes a comprehensive system for color extraction in urban buildings. From the perspective of related research, existing deep learning-related technologies are suitable for various types of recognition, such as architecture and facial recognition, and have achieved good recognition results. This paper selected buildings through deep learning and pattern recognition to extract their colors after selecting them. In the future progression of smart cities, the extracted data results from this system can provide valuable insights for city or regional planning, further enhancing the development of smart cities in terms of visual aesthetics and urban management.