Cubo¶
On-Demand Earth System Data Cubes (ESDCs) in Python
Overview¶
SpatioTemporal Asset Catalogs (STAC) provide a standardized format that describes geospatial information. Multiple platforms are using this standard to provide clients several datasets. Nice platforms such as Planetary Computer use this standard. Additionally, Google Earth Engine (GEE) also provides a gigantic catalogue that users can harness for different tasks in Python.
cubo is a Python package that provides users of STAC and GEE an easy way to create On-demand Earth System Data Cubes (ESDCs). This is perfectly suitable for Deep Learning (DL) tasks. You can easily create a lot of ESDCs by just knowing a pair of coordinates and the edge size of the cube in pixels!
Check the simple usage of cubo with STAC here:
import cubo
import xarray as xr
da = cubo.create(
lat=4.31, # Central latitude of the cube
lon=-76.2, # Central longitude of the cube
collection="sentinel-2-l2a", # Name of the STAC collection
bands=["B02","B03","B04"], # Bands to retrieve
start_date="2021-06-01", # Start date of the cube
end_date="2021-06-10", # End date of the cube
edge_size=64, # Edge size of the cube (px)
resolution=10, # Pixel size of the cube (m)
)
This chunk of code just created an xr.DataArray
object given a pair of
coordinates, the edge size of the cube (in pixels), and additional information to get the
data from STAC (Planetary Computer by default, but you can use another provider!). Note
that you can also use the resolution you want (in meters) and the bands that you require.
Now check the simple usage of cubo with GEE here:
import cubo
import xarray as xr
da = cubo.create(
lat=51.079225, # Central latitude of the cube
lon=10.452173, # Central longitude of the cube
collection="COPERNICUS/S2_SR_HARMONIZED", # Id of the GEE collection
bands=["B2","B3","B4"], # Bands to retrieve
start_date="2016-06-01", # Start date of the cube
end_date="2017-07-01", # End date of the cube
edge_size=128, # Edge size of the cube (px)
resolution=10, # Pixel size of the cube (m)
gee=True # Use GEE instead of STAC
)
This chunk of code is very similar to the STAC-based cubo code. Note that the collection
is now the ID of the GEE collection to use, and note that the gee
argument must be set to
True
.
How does it work?¶
The thing is super easy and simple.
1. You have the coordinates of a point of interest. The cube will be created around these
coordinates (i.e., these coordinates will be approximately the spatial center of the cube).
2. Internally, the coordinates are transformed to the projected UTM coordinates [x,y]
in meters (i.e., local UTM CRS). They are rounded to the closest pair of coordinates
that are divisible by the resolution you requested.
3. The edge size you provide is used to create a Bounding Box (BBox) for the cube in the
local UTM CRS given the exact amount of pixels (Note that the edge size should be a
multiple of 2, otherwise it will be rounded, usual edge sizes for ML are 64, 128, 256,
512, etc.).
4. Additional information is used to retrieve the data from the STAC catalogue or from GEE: starts
and end dates, name of the collection, endpoint of the catalogue (ignored for GEE), etc.
5. Then, by using stackstac
and pystac_client
the cube is retrieved as a
xr.DataArray
. In the case of GEE, the cube is retrieved via xee
.
6. Success! That’s what cubo is doing for you, and you just need to provide the
coordinates, the edge size, and the additional info to get the cube.
Installation¶
Install the latest version from PyPI:
pip install cubo
Install cubo with the required GEE dependencies from PyPI:
pip install cubo[ee]
Upgrade cubo by running:
pip install -U cubo
Install the latest version from conda-forge:
conda install -c conda-forge cubo
Install the latest dev version from GitHub by running:
pip install git+https://github.com/davemlz/cubo
Features¶
Main function: create()¶
cubo is pretty straightforward, everything you need is in the create() function:
da = cubo.create(
lat=4.31,
lon=-76.2,
collection="sentinel-2-l2a",
bands=["B02","B03","B04"],
start_date="2021-06-01",
end_date="2021-06-10",
edge_size=64,
resolution=10,
)
Using different units for edge_size¶
By default, the units of edge_size are pixels. But you can modify this using the units argument:
da = cubo.create(
lat=4.31,
lon=-76.2,
collection="sentinel-2-l2a",
bands=["B02","B03","B04"],
start_date="2021-06-01",
end_date="2021-06-10",
edge_size=1500,
units="m",
resolution=10,
)
- You can use “px” (pixels), “m” (meters), or any unit available in
da = cubo.create(
lat=4.31,
lon=-76.2,
collection="sentinel-2-l2a",
bands=["B02","B03","B04"],
start_date="2021-06-01",
end_date="2021-06-10",
edge_size=1.5,
units="kilo",
resolution=10,
)
Using another endpoint¶
By default, cubo uses Planetary Computer. But you can use another STAC provider endpoint if you want:
da = cubo.create(
lat=4.31,
lon=-76.2,
collection="sentinel-s2-l2a-cogs",
bands=["B05","B06","B07"],
start_date="2020-01-01",
end_date="2020-06-01",
edge_size=128,
resolution=20,
stac="https://earth-search.aws.element84.com/v0"
)
Keywords for searching data¶
You can pass kwargs to pystac_client.Client.search() if required:
da = cubo.create(
lat=4.31,
lon=-76.2,
collection="sentinel-2-l2a",
bands=["B02","B03","B04"],
start_date="2021-01-01",
end_date="2021-06-10",
edge_size=64,
resolution=10,
query={"eo:cloud_cover": {"lt": 10}} # kwarg to pass
)
License¶
The project is licensed under the MIT license.
Logo Attribution¶
The logo and images were created using dice icons created by Freepik - Flaticon.