On-Demand Earth System Data Cubes (ESDCs) in Python

GitHub: https://github.com/davemlz/cubo

Documentation: https://cubo.readthedocs.io/

PyPI: https://pypi.org/project/cubo/

Conda-forge: https://anaconda.org/conda-forge/cubo

Tutorials: https://cubo.readthedocs.io/en/latest/tutorials.html

Paper: https://arxiv.org/abs/2404.13105

News

[!IMPORTANT]
:star: Pinned (2024-04-19): Our cubo paper (preprint) is out in arXiv! Check it here: Montero, D., Aybar, C., Ji, C., Kraemer, G., Sochting, M., Teber, K., & Mahecha, M.D. (2024). On-Demand Earth System Data Cubes.

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:

```python 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 startdate="2021-06-01", # Start date of the cube enddate="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:

```python 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/S2SRHARMONIZED", # Id of the GEE collection bands=["B2","B3","B4"], # Bands to retrieve startdate="2016-06-01", # Start date of the cube enddate="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:

python 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:

python 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, )

[!TIP] You can use "px" (pixels), "m" (meters), or any unit available in scipy.constants.

python 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:

python 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:

python 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.

Citation

If you use this work, please consider citing the following paper:

bibtex @article{montero2024cubo, doi = {10.48550/ARXIV.2404.13105}, url = {https://arxiv.org/abs/2404.13105}, author = {Montero, David and Aybar, César and Ji, Chaonan and Kraemer, Guido and S\"{o}chting, Maximilian and Teber, Khalil and Mahecha, Miguel D.}, keywords = {Databases (cs.DB), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {On-Demand Earth System Data Cubes}, publisher = {arXiv}, year = {2024}, copyright = {Creative Commons Attribution 4.0 International} }

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