## 1 Introduction  
The two main types of earthquake insurance are personal accident insurance and property damage insurance. However, 95% of the casualties in earthquakes are caused by building damage and collapse [1]. The construction of the building exposure database has become a crucial part of the earthquake mitigation work, and its accuracy is related to the emergency response speed of the government and society and the assessment of economic losses from disasters, which further affects the determination of catastrophe insurance rates for insurance companies. This will provide strong data support for disaster prevention before the earthquake and emergency relief and damage assessment after the earthquake [2]. Structural attributes such as the footprint, structure type, use function, story number (height), and construction age of buildings are considered to be the main building seismic information parameters that affect the degree of post-disaster damage to building structures [3; 4; 5; 6; 7; 8]. There are three main methods of obtaining information about buildings: (1) Reasonable speculation based on statistical information and data. For example, Portugal, Greece, Italy, Turkey, and other European countries carry out censuses to collect building information [9]. The Italian National Institute of Statistics (ISTAT) periodically conducts a census of dwellings and provides a set of information on housing, including structural typology, date of construction (or renovation), number of floors, building position in the block, state of repair, and quality of maintenance [10; 11]. Gao et al. established a county-level housing structure database using 1% provincial population sampling survey data for 2005 [12]. (2) Field survey. For example, Di Pasquale et al. and Rosti et al. investigated the damage to buildings after an earthquake in Italy and conducted a vulnerability analysis [13; 14]. Based on the survey of thousands of buildings in hundreds of survey sites in 26 provinces in China, Sun et al. classified mainland China into 12 categories according to regional seismic capacity and developed vulnerability evaluation models for various types of structures in different regions [1]. The statistical information is mainly in the form of data in administrative districts. The use of data usually requires scaling and averaging, but the accuracy of the processed data is reduced, and there is a lack of spatial and temporal properties. Gueguen and Su obtained detailed data such as the construction age, construction materials, and the number of floors of buildings through visits and investigations [15; 16]. Manual survey or mapping data are more accurate but limited by the scope of acquisition, acquisition cost, and other factors. It is difficult to support the construction of a large-scale building exposure database, especially for fast-developing or disaster-prone areas. (3) Extraction based on remote sensing technology, machine learning, and other new technological means. The \"attribute\" type building features that are crucial for vulnerability assessment, such as the distinction of building materials--e.g., masonry/reinforced concrete--or the building age, cannot be easily decided by relying on earth observation data alone, and remote sensing methods should be combined with other sources of information to allow the extraction of relevant vulnerability parameters [5]. In recent years, remote sensing technology, artificial intelligence, and other technologies are increasingly maturing, and the computing power of software and hardware continues to be enhanced. This makes it possible to extract building information on a large scale and with high efficiency [17; 18]. Numerous scholars have accomplished recognition of building outline, roof, and height of buildings to meet different needs and have achieved certain results [2; 19; 20; 21; 22; 23].