## 5 Conclusions  
Geomatic techniques based on open data offer a significant advantage for seismic exposure analysis. This advantage is remarkable in the acquisition and measurement of certain attributes, such as footprint characteristics, the date of first construction, and within-block attributes. Continuous Geospatial Exposure Databases (GEDs) permit the assessment of individual buildings, improving the level of detail of exposure analysis as compared to classical district or neighborhood scale approaches. However, some essential attributes, such as height, are not usually available in high-risk seismic cities and must be obtained at a cost, with periodic actualizations depending on the rate of change of the city. Despite the advantages of geomatic techniques, they cannot completely replace traditional methodologies when performing an exhaustive, accurate seismic exposure study. Geomatic techniques do not directly provide all essential variables in a seismic risk study, as compared to lateral load resisting systems or structural class. A significant sample of buildings is required to validate GED databases and calibrate machine learning models to infer non-measured attributes. The application of geomatic techniques improves the overall study of seismic exposure in several ways, especially in quick assessments, emergencies, and field survey design. GEDs can be used to quickly characterize a city or district in the case of an emergency. They can also be used to optimize the design of field surveys, thus reducing time and costs and optimizing the human and economic resources while maximizing the accuracy of the results. Seismic exposure studies based exclusively on geomatic techniques could be very useful in emergency situations where resources, time, and access to some areas are limited. In these cases, such surveys, while not providing the same amount of structural information as those carried out under optimal conditions, can be crucial for obtaining essential data on seismic exposure at critical times when no other alternative is available. Geomatic techniques employ several technologies that are evolving quickly, as well as data quality, resolution, and availability. The methodology developed in this study is designed to integrate all available information about seismic exposure of an area into GEDs, in this case, the canton of San Jose in Costa Rica. We expect that the evolution in methods, data availability, and machine learning will improve the methods and the quality of the attributes in the GED and even allow for the estimation of new attributes of buildings that cannot currently be obtained. Conceptualization, [PERSON], [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; validation, [PERSON], [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON]; investigation, [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received public funds from the Comunidad de Madrid Industrial Doctorate Program (IND2023/TIC-28743 and IND2020/TIC-17528), the \"Proyecto de Cooperacion Triangular UE-Costa Rica-ALC Adelante-2 Construccion sostenible y resilienten Centroamerica y el Caribe ante la amenaza sismica: cooperacion regional basada en la experiencia de Costa Rica\", and the SIAGUA Project, reference PID2021-128123 OB-C22, funded by MCIN, Spain/AEI, Spain/FEDER, UE. **Data Availability Statement:** The datasets used in this study can be found in online repositories.