## 1 Introduction  
Understanding seismic exposure is fundamental for comprehensive seismic risk assessments, which are essential for reducing the human and economic losses associated with earthquakes. Although global mortality from natural disasters has decreased in recent decades, earthquakes remain as particularly devastating events. The catastrophic earthquakes of 2004 and 2010 alone accounted for 93% and 69% of disaster-related deaths, respectively, in those years [1]. Between 1900 and 2019, seismic events caused approximately 2.6 million deaths globally and generated USD 828 billion in economic losses, ranking as the third and second most significant causes of disaster mortality and damage, respectively [2]. Given the inability to predict the time, location, and magnitude of earthquakes, reduction through exposure assessment and preparedness is paramount. Seismic exposure refers to the quantification of assets, such as populations, buildings, and infrastructure, located in hazard-prone areas, that are potentially vulnerable to seismic impacts. As a key component of seismic risk analysis, exposure studies rely on the accurate identification of building characteristics that influence structural performance during earthquakes. Traditionally, these assessments have been conducted through field surveys and extrapolation techniques, which, although informative, are often limited by low spatial resolution (district or neighborhood scales) and high resource demands. In recent years, there has been rapid advancement and widespread adoption of geospatial or geospatial technologies, which provide a broad range of tools and methodologies for acquiring, processing, analyzing, and visualizing spatially referenced data. These techniques have become especially relevant with the development of Geospatial Databases (GDs), which facilitate more efficient data acquisition and management [3]. The use of geospatial technologies (GTs) is now recognized as a fundamental phase prior to field survey design, allowing for the selection of optimized sampling areas [4]. These advancements support the extraction of critical building attributes at various spatial scales, from census sections and urban blocks to individual buildings. The Global Earthquake Model (GEM) framework identifies 13 essential attributes for assessing a building's seismic vulnerability [5]. GTs have proven particularly effective in directly obtaining several of these attributes, such as the building height, the construction or retrofit date, structural irregularity, orientation, and the building plan shape. Additional attributes, like the lateral load-resisting system or the roof system material, can be inferred from secondary variables derived from remote sensing or ancillary data sources. All of these attributes can be integrated into Geospatial Exposure Databases (GEDs), enabling more comprehensive and scalable seismic risk modelling [6]. Nevertheless, certain attributes, such as exterior wall materials, foundation types, or floor systems, still require field verification. To overcome this limitation, probabilistic methodologies have been proposed to estimate non-surveyed building characteristics based on nearby sampled structures, significantly reducing uncertainty and enhancing accuracy [7]. The foundational step in exposure assessment involves obtaining detailed spatial footprint coverages of buildings at the desired scale. These footprints are typically digitized manually [8] using high-resolution orthophotos and LiDAR (Light Detection and Ranging) models [9] derived from UAV (Unmanned Aerial Vehicle) or aerial platforms, as well as satellite imagery [10]. The building height can be retrieved from 3D point clouds generated through photogrammetry or LiDAR [11] or derived from satellite data sources such as TanDEM-X, Sentinel-2 [12], or even Google Street View [8], with the vertical resolution ranging from centimeters to entire floors.