geetiles 0.1.1

download Google Earth Engine datasets to tiles as geotiff arrays

geetiles

download Google Earth Engine datasets to tiles as geotiff arrays

Uses the Google Earth Engine High Volume Endpoint which, according the doc

This service is designed to support a much larger number of simultaneous requests per user, but provides less caching, so it's best for small queries that don't involve any sort of aggregation (like fetching tiles from pre-built images).

install with

pip install geetiles

1. create grid on a given area of interest as wkt

geet grid --aoi_wkt_file luxembourg.wkt  --chip_size_meters 1000 --aoi_name lux --dest_dir .

you can find the file luxembourg.wkt under data. Usually you would have to provide your own with your area of interest, with coordinates expressed in WSG84 degrees lon/lat.

this generates file ./lux_partitions_aschips_14c55eb7d417f.geojson. Use a tool such as QGIS to view it.

2. download tiles

geet download --tiles_file lux_partitions_aschips_14c55eb7d417f.geojson  --gee_image_pycode 'sentinel2-rgb-median-2020' --dataset_name s2 --pixels_lonlat [100,100] --skip_if_exists --skip_confirm

this fills the folder lux_partitions_aschips_14c55eb7d417f/s2 with RGB geotiff images of size 100x100 pixels.

If using sentinel2-rgb-median-2020 as gee_image_pycode, which is an alias to Sentinel-2 MSI Level 2-A GEE dataset, taking the median of the cloudless chips over the year 2020.

If using esa-world-cover as gee_image_pycode, which is an alias to ESA WorldCover 10m v100 GEE dataset.

Other usages

Other ways to create the set of tiles (shapes)

• As random partitions with at most 5km size length.

  geet random --aoi_wkt_file luxembourg.wkt  --max_rectangle_size_meters 20000 --aoi_name lux --dest_dir .
  

• Using the reference administrative divisions in at EU Eurostat

  geet select --orig_shapefile COMM_RG_01M_2016_4326.shp --aoi_wkt_file notebooks/luxembourg.wkt --partition_name comms --aoi_name lux --dest_dir .
  

Using your own code to define the GEE source image object.

geet download --tiles_file lux_partitions_aschips_14c55eb7d417f.geojson  --gee_image_pycode crops.py --dataset_name crop --pixels_lonlat [100,100] --skip_if_exists --skip_confirm --n_processes 20

assuming the file crops.py contains the following code

    import ee

    def get_ee_image():
        return ee.Image('USGS/GFSAD1000_V1')\
                 .select('landcover')\
                 .visualize(min=0.0, max=5.0,
                            palette = ['black', 'orange', 'brown', 
                                       '02a50f', 'green', 'yellow'])

The crops.py will be saved under the destination folder for reference. The destination folder is created alongside the tiles-file.