## 4 Algorithm Application for Diverse Data Sources
### Algorithm Optimization Under Different Data Sources  
In the observation of urban residential vacancy rate, according to the characteristics of various data sources, algorithm optimization becomes the key to improving the observation accuracy and efficiency [101]. Various data sources include but are not limited to remote sensing satellite images, street view images, real estate transaction records, power grid water data and community census information. These data have their own characteristics in terms of dimension, accuracy, timeliness, etc. In view of these characteristics, a series of optimizations have been carried out on the algorithm application to adapt to the challenges of complex environment and urban development needs. First, for the remote sensing satellite image data source, the optimization algorithm mainly focuses on image analysis and information extraction [102]. By using deep learning techniques, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), residential areas can be effectively identified and classified, and the characteristics of vacant houses can be extracted. At the same time, combined with multi-temporal remote sensing data, change detection algorithms are used to monitor the dynamic changes of residential areas to more accurately judge the vacancy status. In addition, in view of the impact of different seasons and weather conditions on image quality, the optimization algorithm uses image enhancement and denoising technology to improve the stability and reliability of observation data. Secondly, street view images are another important data source, and their algorithm optimization focuses on image recognition and scene understanding. Using target detection technologies in deep learning, such as YOLO (You Only Look Once) and Faster R-CNN (Faster Region-based Convolutional Neural Network), residential buildings and their surroundings can be accurately identified, and the vacancy status can be inferred by analyzing the characteristics of the residential appearance and the surrounding environment. At the same time, combined with geographic information system (GIS) data, the optimization algorithm can more accurately locate and analyze the spatial distribution characteristics of urban residential vacancies. This method must also include the advantages and limitations of the data and methods used in this study. The optimization of the street-view image algorithm utilizes object-detection techniques in deep learning (such as YOLO and Faster R-CNN). It can accurately identify residential buildings and their surrounding environments. By combining with GIS data, it can more precisely locate and analyze the spatial distribution characteristics of urban residential vacancies, providing detailed information for urban planning and management. However, this algorithm still has certain limitations. The collection scope of street-view images is limited, making it difficult to cover all areas of the city, thus creating data blind spots. Image recognition and scene understanding are greatly affected by factors such as image quality, shooting angle and lighting conditions. Under some special circumstances (such as bad weather or at night), the recognition accuracy will decline. Inferring the vacancy status from street-view images is mainly based on appearance and surrounding-environment features. It is difficult to obtain information about some internal facility conditions, which may lead to inaccurate judgments of the vacancy status. In terms of real estate transaction records, data mining and machine learning algorithms have become the main tools [103; 104]. By analyzing historical transaction data, clustering algorithms are used to identify the potential patterns of vacant homes, and time series analysis is combined to predict the trend of vacancy rates. In addition, natural language processing technology is used to perform sentiment analysis and keyword extraction on real estate advertisement texts to provide auxiliary information for judging the vacancy status of homes.