## 1 Introduction
However, these methods are limited in extraction efficiency and result accuracy when applied to a large area by factors such as sample quantity and quality, model structure, and parameter adjustment. In addition, it is difficult to obtain key information such as structure type and use function of buildings by relying only on remote sensing data due to limitations such as data dimensionality and identification methods [24]. With the research and application of deep network data, such as AOI (Area of Interest), POI (Point of Interest), and public management information, many scholars have carried out a series of research work on housing property information because of the advantages of the fast update cycle and low comprehensive cost and have achieved better results. For example, Xu et al. established a novel method for building height calculation for an urban area based on a mask region-based convolutional neural network (Mask R-CNN) model and street view image [25]. Some scholars used POI data and adopted certain methods to achieve the quantitative identification of neighborhood functions [26; 27; 28]. Gu et al. used POI and AOI data to determine and classify building functions [29]. Qi et al. extracted the building footprint, number of floors, and structure type from Google Earth images using high-resolution Google Earth images, Tencent/Baidu Street View, crowdsourced data, and relevant building-related local knowledge [30]. The above methods have achieved some practical research results for specific data and problems, but they also have shortcomings. Most studies rely on a single data source and research method, focusing on the extraction of a single building parameter, which is inefficient and has a small scope of application. In addition, most studies focus more on the identification of building footprint, while the research results on the extraction of attribute information such as the story number, structure, age, and use function of buildings are limited. Building exposure databases serving disaster management and earthquake insurance cannot be built by relying only on a single parameter. In summary, the establishment of a building exposure database for housing requires the integration of multiple data, methods, and related domain knowledge. There are significant differences between urban and rural development in China, and the housing exposure database should be constructed separately according to rural and urban areas. In this paper, based on various data and extraction methods and taking urban areas as the object of specific research, the research was conducted on the seismic information extraction of buildings in urban areas for earthquake insurance. Firstly, building footprint data were obtained based on remote sensing data using a deep-learning method. Then, multi-source data such as public data, deep network data, crowdsourcing data, and spatiotemporal data were analyzed and screened, and building function information, story number (or height), structure type, and construction age data were obtained through spatial overlay and kernel density analysis, Kriging interpolation, and other methods. Finally, the extraction effect was verified.