Martin Christen: Creating 3D Maps using Python

Summarized Points

• The speaker uses publicly available satellite imagery and elevation data to create 3D maps using Python.
• The process involves collecting data from sources like Open Knowledge Foundation, Data World, and shuttle weather topography mission (SRTM) for global datasets.
• Rasterio is used to read geospatial rasters, and GDAL is employed to create hillshades and reproject images.
• Elevation data is obtained from global datasets, including SRTM and Orthophotos.
• The speaker uses Landsat and other satellite images to create extensive datasets.
• While some data is available for free, governments often charge for it, but more are providing open data.
• Processing large datasets can take time, and some manual examination is required.
• The speaker recommends using the Wavefront OBJ format for 3D models and cites instances like OpenStreetMap for obtaining data.
• Use cases include solar panel planning, with 3D mapping still being niche but advancing.
• Processing time can be significant, but the speaker has managed to reduce it through parallel processing and other optimizations.
• Some governments provide data in response to crises, such as conflicts, and open data is becoming more widely available.