## 4 Discussion  
The methodology presented in this paper fulfils the objective of improving the technical and economic efficiency of seismic exposure studies in areas with high risk and data scarcity by reducing time and costs. This objective is achieved, both in the reduction in time and costs for the assessment of seismic exposure and the successful classification of building typologies from the GED attributes. Regarding times and costs, the GED methodology is more efficient than the most intensive REM and full survey methodologies. The cost per square kilometer of the GED methodology is 12% and 2% of the costs of REM [44] and full survey methods [60], respectively. However, the level of confidence is higher in full survey and REM methods that include a visual human assessment of the surveyed buildings. It is necessary to validate the accuracy and reliability of the GED approach, which implies the contrast with a representative sample of buildings obtained with a more detailed methodology. It is evident that a highly detailed database enhances the precision and reliability of large-scale seismic vulnerability assessments. However, this level of detail typically requires substantial resources, making such an approach impractical for extensive areas. Therefore, it becomes necessary to strike a balance between the accuracy and reliability of the data and the limitations imposed by available resources. The case study of San Jose was been carried out with the validation database of 7296 buildings obtained with REM methodology. A critical evaluation of the advantages and limitations of the proposed methodology is necessary, particularly through a comparison with similar approaches previously presented in the literature. To this end, there are many references in the literature of data-collection strategies aimed at creating GIS databases [11] and at characterizing the typical building typologies found in historical urban centers [24]. Nevertheless, most of these methodologies were developed in data-rich environments, with freely available updated and detailed cadasters [43], GIS databases [72], orthophotos and LiDAR data [9], and remote sensing data [73] developed in Europe (mainly in Italy), North America [18,19], Japan [20], and, more recently, in China [21]. Most efforts in the literature support the development of a comprehensive urban-level database, implemented within a GIS environment, to facilitate subsequent seismic vulnerability assessments [6,46]. The integration of diverse data sources is an effective means to derive comprehensive urban-scale information, which is crucial for identifying representative building typologies and associated characteristics. The application of GIS  
Figure 17: Graph of influence of attributes on class assignment to buildings using geospatial attributes with Random Forest classifier RF2, including building height. tools plays a key role in organizing and managing this extensive dataset, thereby enhancing the interpretability and utility of the database. The estimation of attributes using GTs is first and foremost influenced by data availability and data quality. Not all data sources are available and openly accessible everywhere in the world. The gathering of sufficient quality baseline data to conduct such studies will directly influence the quality of the results. Once the information gathering is complete, some attributes can be obtained directly, but others require a process that may increase the uncertainty of the characterization. The footprints of buildings are essential for the creation of a GED. In addition, the correct digitization of building footprints clearly improves the quality and accuracy of attributes obtained for the buildings, such as the floor area of each building. Building footprints are measured by hand in many studies and by many local authorities, as in the case of the Municipality of San Jose. However, many studies have attempted to develop new methodologies aimed at automating, albeit partially, the process of obtaining these footprints.