Contents

Details

Analytics

Home

All Products

Docs

Source Cooperative is a Radiant Earth project

Saint Lucia 2022 10cm Aerial Imagery · Oxford Programme for Infrastructure Systems · Source Cooperative | Source Cooperative

Saint Lucia 2022 10cm Aerial Imagery

Name: Saint Lucia 2022 10cm Aerial Imagery
Creator: Oxford Programme for Infrastructure Systems

2022 Saint Lucia Imagery 10cm from geoservices.govt.lc redistributed as covered under Government of St Lucia's Open Data Policy

Preview using https://source-cooperative.github.io/cog-viewer/.

This is a ~280GB Cloud-Optimised Geotiff, you may not want to download all of it.

Access in QGIS

Open a QGIS project.

Go to Layer > Add Layer > Add Raster Layer
Under Source Type, select "Protocol: HTTP(S), cloud, etc."
Under Protocol, select Type as "HTTP/HTTPS/FTP"
In the URI box, paste the URL: https://data.source.coop/opsis-oxford/st-lucia-imagery-2022/orthophotography-2022.tif
No authentication is needed
Click "Add" to add the layer. It should load overviews at low zoom levels, and full detail up to the ~10cm native pixel resolution.

Access through GDAL

Use the vsicurl protocol with gdal commands, e.g.:

gdal raster info /vsicurl/https://data.source.coop/opsis-oxford/st-lucia-imagery-2022/orthophotography-2022.tif

Should show metadata about the file.

Access in Python

Using, for example, rioxarray, xarray and dask, the data can be lazily loaded for distributed computation or subsetting, without materialising the full download:

Should output something like this (note the >400GB array size - this is lazily loaded!):

1import rioxarray
2import xarray as xr
3cog_url = "https://data.source.coop/opsis-oxford/st-lucia-imagery-2022/orthophotography-2022.tif"
4ds = rioxarray.open_rasterio(cog_url, chunks="auto")
5print(ds)

1import rioxarray
2import xarray as xr
3cog_url = "https://data.source.coop/opsis-oxford/st-lucia-imagery-2022/orthophotography-2022.tif"
4ds = rioxarray.open_rasterio(cog_url, chunks="auto")
5print(ds)

1<xarray.DataArray (band: 4, y: 455000, x: 235000)> Size: 428GB
2dask.array<open_rasterio-4aa923b42b1970035240f8a0ec664abf<this-array>, shape=(4, 455000, 235000), dtype=uint8, chunksize=(1, 11264, 11264), chunktype=numpy.ndarray>
3Coordinates:
4  * band         (band) int64 32B 1 2 3 4
5  * y            (y) float64 4MB 1.561e+06 1.561e+06 ... 1.516e+06 1.516e+06
6  * x            (x) float64 2MB 7.07e+05 7.07e+05 ... 7.305e+05 7.305e+05
7    spatial_ref  int64 8B 0
8Attributes:
9    AREA_OR_POINT:  Area
10    _FillValue:     0
11    scale_factor:   1.0
12    add_offset:     0.0

1<xarray.DataArray (band: 4, y: 455000, x: 235000)> Size: 428GB
2dask.array<open_rasterio-4aa923b42b1970035240f8a0ec664abf<this-array>, shape=(4, 455000, 235000), dtype=uint8, chunksize=(1, 11264, 11264), chunktype=numpy.ndarray>
3Coordinates:
4  * band         (band) int64 32B 1 2 3 4
5  * y            (y) float64 4MB 1.561e+06 1.561e+06 ... 1.516e+06 1.516e+06
6  * x            (x) float64 2MB 7.07e+05 7.07e+05 ... 7.305e+05 7.305e+05
7    spatial_ref  int64 8B 0
8Attributes:
9    AREA_OR_POINT:  Area
10    _FillValue:     0
11    scale_factor:   1.0
12    add_offset:     0.0