Copernicus DEM — download & crop¶

Fetch a 1-degree GLO-30 elevation tile from the AWS Open Data mirror, cropped to a small AOI, and inspect the resulting WGS84 COG with pyramids.

Setup¶

A couple of imports: tempfile / Path give us a scratch directory for the downloaded tile, and EarthLens is the unified entry point.

In [ ]:

import tempfile
from pathlib import Path

from earthlens import EarthLens

import tempfile from pathlib import Path from earthlens import EarthLens

Build the request¶

The Copernicus DEM lives on a public AWS bucket, so we use the amazon-s3 backend. We point it at the copernicus-dem dataset, a small AOI off the Gulf of Guinea, and a scratch output directory.

In [ ]:

out = Path(tempfile.mkdtemp(prefix='dem_'))
src = EarthLens(
    data_source="amazon-s3",
    start='2021-01-01',
    end='2021-01-01',
    aoi=[6.40, 0.40, 6.45, 0.45],
    dataset='copernicus-dem',
    path=str(out),
)

out = Path(tempfile.mkdtemp(prefix='dem_')) src = EarthLens( data_source="amazon-s3", start='2021-01-01', end='2021-01-01', aoi=[6.40, 0.40, 6.45, 0.45], dataset='copernicus-dem', path=str(out), )

Download the tile¶

download() fetches the cropped tile to out and returns the list of written paths.

In [ ]:

paths = src.download(progress_bar=False)
paths

paths = src.download(progress_bar=False) paths

Inspect the result¶

pyramids reads the written COG so we can confirm its CRS, bounding box, and shape match the requested AOI.

In [ ]:

from pyramids.dataset import Dataset

ds = Dataset.read_file(str(paths[0]))
print('CRS EPSG:', ds.epsg)
print('bbox:', [round(x, 3) for x in ds.bbox])
print('shape:', ds.shape)

from pyramids.dataset import Dataset ds = Dataset.read_file(str(paths[0])) print('CRS EPSG:', ds.epsg) print('bbox:', [round(x, 3) for x in ds.bbox]) print('shape:', ds.shape)