## 4 Discussion
Currently, these novel methodologies have not yielded products that provide an adequate level of detail, and therefore it is often preferable to produce these footprints manually. In a recent study in Haiti [11], footprints were obtained through both semi-automatic and manual methods, and in the end, the manually produced footprints were used. The estimation of building heights involves an investment in many parts of the world, requiring photogrammetry or LiDAR acquisition and processing [9]. Traditionally, point clouds were obtained by photogrammetry from aerial flights, although in recent years, drone flights are the main source, if permitted by the local regulations, making this attribute more affordable. The increased development of machine learning for image analysis is enabling the extraction of measurements from in-street images, as proposed by [PERSON] et al. [74]. This technology is limited to the availability of training sets of images, adapted to the reality of the city landscape in every part of the world. Street-view imagery repositories are increasing worldwide, such as Google Street View (not open) [75] and Mapillary (open) [76], but their coverage is not uniform in space nor in time. The height of buildings is one of the more determinant parameters in the process of classifying BTs, as it directly affects the behavior of a structure in a seismic event. A study carried out in the city of Osijek, Croatia, [16] concluded that it is one of the basic parameters for the characterization of a building by means of GEM taxonomy, together with the lateral load-resisting system. New methodologies are being developed for the estimation of height, in meters or in the number of levels, among which there is an increasing interest in the analysis of street-view-like imagery, either manually [8] or automatically [74]. The determination of the roofing material has been carried out traditionally by visual assessment in the field or orthoimages [11]. In this research, we successfully adapted the methodologies proposed by [PERSON] et al. [13; 14] to obtain the roof covering material in San Jose from hyperspectral imagery. In case ASTER or other hyperspectral sources are not available, Sentinel-2 is the open recommended source, although the radiometric resolution is worse, so an increase in the error of assignment is expected. The calibration of Random Forest algorithms [71] is of utmost importance with regard to the usefulness of the results obtained and as a source of information for other phases of the seismic risk study. The technology used in the process of predicting and assigning a class to buildings is constantly evolving and improving, thus allowing better results to be obtained and reducing the margins of error. It is also necessary to consider the specific characteristics of each city, the regulations, and any other circumstances that allow for the definition of additional compatibility criteria between attributes identified using geomatic techniques and those estimated during the process of BT assignment. As stated by [PERSON] [77], the use of the GEM taxonomy to analyze the characteristics of buildings through the proposed attributes and the subsequent classification of structures provides a methodology that has been and continues to be developed in many places in recent years. The associated challenges are related to the adoption of new geospatial and computational techniques to enhance this methodology. In this case, it was decided that buildings would be assigned to a given class using a Random Forest algorithm. This algorithm is suitable for class assignment problems such as those encountered in the study of seismic exposure. However, the evolution of machine learning is leading to new products which further improve the benefits of these algorithms.