You have exercise_01.xdf containing blue and red force movements. You need to show the 2-hour engagement in Google Earth.
for idx, row in df.iterrows(): point = kml.newpoint(name=row['entity_name']) point.coords = [(row['longitude'], row['latitude'])] point.description = f"Altitude: row['altitude'] | Speed: row['speed']"
QGIS (free, open-source), ArcGIS Pro (commercial), Global Mapper (commercial).
FME by Safe Software has a reader for XDF (especially variants used in simulation). FME Workbench allows a visual drag-and-drop transformation from XDF to KML, including complex schema mapping. It is expensive but the most reliable for mission-critical conversions. Challenge 1: Coordinate System Mismatch XDF sometimes uses MGRS (Military Grid Reference System) or UTM (Universal Transverse Mercator), while KP requires geographic coordinates (latitude/longitude in WGS84). Solution: Use QGIS or GDAL’s gdaltransform to reproject before exporting to KML. Challenge 2: Temporal Data Loss XDF often includes timestamps for each track point (simulating movement). Standard KML does not inherently play animations unless you use <gx:Track> (Google Earth extension). Solution: In QGIS, use the Time Manager plugin to create animated KML tracks. In Python, use simplekml with gxtrack = kml.newgxtrack() . Challenge 3: Attributes and Styling KP supports basic styling (colors, icons). If your XDF contains complex styling (e.g., military symbology based on APP-6), you will need to manually map those to KML <Style> tags. Challenge 4: Binary XDF Without Documentation Some proprietary XDF formats are encrypted or undocumented. In this case, contact the vendor of the simulation system (e.g., VT MÄK, CAE) for an export to an intermediate format like CSV or Shapefile, then convert to KP. Part 4: Step-by-Step Example – Converting a JCATS XDF Log to KML Let’s walk through a real-world XDF to KP conversion using a JCATS exercise log.
