Mapchete processes raster and vector geodata.

Processing larger amounts of data requires chunking the input data into smaller tiles and process them one by one. Python provides a lot of useful packages to process geodata like shapely or numpy.

With the help of fiona and rasterio Mapchete takes care about resampling and reprojecting geodata, applying your Python code to the tiles and writing the output either into a single file or into a directory of files organized in a WMTS-like tile pyramid. Details on tiling scheme and available map projections are outlined in Tiling and projections.

The code is available under the MIT License.


A process creating a hillshade from an elevation model and clipping it with a vector dataset could look like this:

# content of hillshade.mapchete
process: hillshade.py
    min: 0
    max: 12
    dem: /path/to/dem.tif
    land_polygons: /path/to/polygon/file.geojson
    format: PNG_hillshade
    path: /output/path
    grid: mercator

# process specific parameters
resampling: cubic_spline
# content of hillshade.py

def execute(mp, resampling="nearest"):

    # Open elevation model.
    with mp.open("dem") as src:
        # Skip tile if there is no data available or read data into a NumPy array.
        if src.is_empty(1):
            return "empty"
            dem = src.read(1, resampling=resampling)

    # Create hillshade using a built-in hillshade function.
    hillshade = mp.hillshade(dem)

    # Clip with polygons from vector file and return result.
    with mp.open("land_polygons") as land_file:
        return mp.clip(hillshade, land_file.read())

Examine the result in your browser by serving the process by pointing it to localhost:5000:

$ mapchete serve hillshade.mapchete

If the result looks fine, seed zoom levels 0 to 12:

$ mapchete execute hillshade.mapchete --zoom 0 12


Indices and tables