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

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<a href="https://github.com/davemlz/cubo"><img src="https://github.com/davemlz/cubo/raw/main/docs/_static/logo.png" alt="cubo"></a>


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


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---

**GitHub**: [https://github.com/davemlz/cubo](https://github.com/davemlz/cubo)

**Documentation**: [https://cubo.readthedocs.io/](https://cubo.readthedocs.io/)

**PyPI**: [https://pypi.org/project/cubo/](https://pypi.org/project/cubo/)

**Conda-forge**: [https://anaconda.org/conda-forge/cubo](https://anaconda.org/conda-forge/cubo)

**Tutorials**: [https://cubo.readthedocs.io/en/latest/tutorials.html](https://cubo.readthedocs.io/en/latest/tutorials.html)

**Paper**: [https://doi.org/10.1109/IGARSS53475.2024.10640742](https://doi.org/10.1109/IGARSS53475.2024.10640742)

---

## News

> [!IMPORTANT]  
> :star: **Pinned (2026-02-12)**: Our `cubo` paper is out in IEEE! 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.](https://doi.org/10.1109/IGARSS53475.2024.10640742)**

## Overview

[SpatioTemporal Asset Catalogs (STAC)](https://stacspec.org/) 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](https://planetarycomputer.microsoft.com/) use this standard.
Additionally, [Google Earth Engine (GEE)](https://developers.google.com/earth-engine/datasets/) 
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
    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)
)
```

![Cubo Description](https://github.com/davemlz/cubo/raw/main/docs/_static/cubo_desc.png)

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/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:

```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:

> [!TIP]
> You can use "px" (pixels), "m" (meters), or any unit available in [`scipy.constants`](https://docs.scipy.org/doc/scipy/reference/constants.html#units).

### 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
@inproceedings{montero2024cubo,
  title = {On-Demand Earth System Data Cubes},
  url = {http://dx.doi.org/10.1109/IGARSS53475.2024.10640742},
  DOI = {10.1109/igarss53475.2024.10640742},
  booktitle = {IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium},
  publisher = {IEEE},
  author = {Montero,  David and Aybar,  César and Ji,  Chaonan and Kraemer,  Guido and S\"{o}chting,  Maximilian and Teber,  Khalil and Mahecha,  Miguel D.},
  year = {2024},
  month = jul,
  pages = {7529–7532}
}
```

## Logo Attribution

The logo and images were created using <a href="https://www.flaticon.com/free-icons/dice" title="dice icons">dice icons created by Freepik - Flaticon</a>.

[![RSC4Earth](https://github.com/davemlz/cubo/raw/main/docs/_static/esds.png)](https://rsc4earth.de/authors/esds/)

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