## 4 GIS Integration
The first map integrated into our geodatabase is the topographic map, which contains the height \(h\) at each point of the surface \(S\), defined by the mapping given by Equation (15). This information can be found in any Digital Elevation Model (DEM), and we initially selected the DEM also published by the IGN via a Web Coverage Service (WCS) [52] with a resolution of 25 m, currently deprecated. Today, cartographic products with the same purpose and resolutions of 2 [53] and 5 [54] meters are available. The topographic map with a 5 m resolution is the most-appropriate option for PhyFire's features. The second map collects all the elements involving the function of either artificial or natural fuelbreaks that affect the fire spread. These were initially extracted from the Spanish Land Cover Information System (SIOSE) [55] by selecting all areas where a fire cannot occur (barren land, water bodies, transport infrastructures, etc. ), providing zero-load fuel data for the model. It should be noted that forest fires simulated with the PhyFire model can cross the area associated with these elements thanks to the modeling of the radiation process. Today, new versions of the SIOSE product [56] together with other highly detailed products can be used for the same purpose, such as the National Topographic Database at a 1:25,000 scale (BTN25) [57], or the collaborative project OpenStreetMap [58]. It is possible to include temporal scenarios, especially those related to the different states of the crops, by using satellite data and the estimation of different spectral indexes such as the NDVI [59] or the NDBR [60]. The third map gathers all the information related to the fuel type, which is especially important for the PhyFire model, as most of its input variables depend on the fuel type (see Table 2). The PhyFire model can be tuned to work with different fuel classification systems, such as the Northern Forest Fire Laboratory (NFFL) system [61], the Fire Behavior Fuel Models (FBFM) [62], and of course, the Mediterranean-European Prometheus system [63], or even monitored data whenever available. In Spain, depending on the region and/or year in which the fire to be simulated occurred, the fuel type map used by PhyFire was processed by assembling the Spanish Forestry Map 1:25,000 (MFE25) [64] and the Spanish Forestry Map 1:50,000 (MFE50) [65]. Some limitations of this type of information are its lack of update, the lack of information in some areas, the non-consideration of the seasonal nature of forest fuel, or the subjective interpretation in the field work when this information is gathered. It is possible to solve these problems by using satellite data [63] for the specific instants when the simulation is going to be carried out and algorithms of machine learning [66] to keep the fuel classification updated. The pre-processing of the geospatial input data and the post-processing of the simulation results can slow down the simulation process due to the high amount of information to be handled. New improvements have been developed to further enhance the efficiency of the simulation system embedding part of the pre- and post-processing functionalities through the integration of GDAL/OGR [50] utilities in the original code of the PhyFire model.