# City Architectural Color Recognition Based on Deep Learning and Pattern Recognition
## 1 Introduction  
China's White Paper on Smart Buildings, published in July 2021, emphasized the need to promote digital innovation and building scenarios for smart cities and strengthen digital applications. In the development and construction of cities, the forms of the development of buildings, including their color, shape, and arrangement, significantly shape the urban landscape and play a crucial role in facilitating its growth. Within the scope of design psychology, color holds particular importance as it influences the perception and image of a building, ultimately reflecting the character and identity of a city. Therefore, the use of image-recognition technology in the detection and analysis of the colors of urban buildings is an efficient way to understand a city's impression. Providing valuable data and theoretical support for future urban development and planning endeavors, it has become a crucial step in developing smart cities. Existing research in this field can be categorized into three main areas. The first area focuses on building recognition within the context of smart city planning. [PERSON] et al. [1] discovered that identifying model buildings based on their plan outline features partially reflects the functional characteristics of said buildings. In their study, they identified features of the building's texture and subsequently classified the building. Additionally, [PERSON] et al. [2] researched the use of satellite images to extract building contours and classify urban areas into urban and rural construction zones, studying the urban morphology within these areas. However, the proposed region extraction algorithmin their study was found to be complex and have a high demand for accurate building region identification. In the area extraction algorithm, the authors proposed a relatively cumbersome approach with high accuracy requirements, requiring extensive training to ensure the accuracy of the building area extraction. The second area of research concentrates on algorithms for building recognition. [PERSON] et al. [3] employed MAEU-CNN technology to improve building recognition. However, they encountered the challenges of under-classification and misclassification during the training process, causing the authors to explore the incorporation of building color and shape as additional elements for classification. In a separate study, [PERSON] et al. [4] proposed a CNN framework that successfully retrieved and differentiated building edges, overcoming limitations in image recognition clarity. Similarly, [PERSON] et al. [5] classified buildings based on two benchmarks: Functional purpose and architectural style. They established a predetermined hierarchy within the overall model and differentiated buildings based on color identification. The final area of research focuses specifically on the identification of building colors. [PERSON] et al. [6] collected data on the colors of residential areas in the functional core of Beijing using GIS imagery, aiming to predict the overall color composition of the area based on the colors of the collected buildings. However, their study lacked a detailed analysis of the specific colors and details of the buildings within the area. Another study conducted by [PERSON] et al. [7] highlighted the potential for the positioning of original buildings based on their exterior color. Based on convolutional neural networks, [PERSON] and his team identified regional buildings, pinpointing the positional differences between new and older structures within images [8]. Considering the social background and existing research, the extraction of architectural colors holds significant importance in developing smart cities. These extracted colors have diverse applications, including architectural recognition, urban form evaluation, and urban planning. However, previous research has primarily focused on utilizing color elements to assist in building edge detection or reverse positioning of buildings based on key colors.