SatExtractor

Build, deploy and extract satellite public constellations with one command line.

• tldr: SatExtractor gets all revisits in a date range from a given geojson region from any public satellite constellation and store it in a cloud friendly format.

The large amount of image data makes it difficult to create datasets to train models quickly and reliably. Existing methods for extracting satellite images take a long time to process and have user quotas that restrict access.

Therefore, we created an open source extraction tool SatExtractor to perform worldwide datasets extractions using serverless providers such as Google Cloud Platform or AWS and based on a common existing standard: STAC.

The tool scales horizontally as needed, extracting revisits and storing them in zarr format to be easily used by deep learning models.

It is fully configurable using Hydra.

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SatExtractor needs a cloud provider to work. Before you start using it, you'll need to create and configure a cloud provider account.

We provide the implementation to work with Google Cloud, but SatExtractor is implemented to be easily extensible to other providers.

The package is structured in a modular and configurable approach. It is basically a pipeline containing 6 important steps (separated in modules).

• Builder: contains the logic to build the container that will run the extraction.

  more info

  SatExtractor is based on a docker container. The Dockerfile in the root dir is used to build the core package and a reference in it to the specific provider extraction logic should be explicitly added (see the gcp example in directory providers/gcp).

  This is done by setting ENV PROVIDER var to point the provider directory. In the default Dockerfile it is set to gcp: ENV PROVIDER providers/gcp .

• Stac: converts a public constellation to the STAC standard.
  more info

  If the original constellation is not already in STAC standard it should be converted. To do so, you have to implement the constellation specific STAC conversor. Sentinel 2 and Landsat 7/8 examples can be found in src/satextractor/stac . The function that is actually called to perform the conversion to the STAC standard is set in stac hydra config file ( conf/stac/gcp.yaml )

• Tiler: Creates tiles (patches) of the given region to perform the extraction.
  more info

  The Tiler split the region in tiles using SentinelHub splitter . For example if a Tile size of 10000m is set, you will have in your storage patches of size 10000m. The config about the tiler can be found in conf/tiler/utm.yaml . There, the size of the tiles can be specified.

• Scheduler: Decides how those tiles are going to be scheduled creating extractions tasks.

  more info

  The Scheduler takes the resulting tiles from the Tiler and group them in bigger areas to be extracted.

  For example, if the Tiler splitted the region in 1000x1000m tiles, now the scheduler can be set to group them in UTM splits of, say, 100000x100000m (100km). Also, the scheduler calculates the intersection between the patches and the constellation STAC assets. At the end, you'll have and object called ExtractionTask with the information to extract one revisit, one band and multiple patches. This ExtractionTask will be send to the cloud provider to perform the actual extraction.

  The config about the scheduler can be found in conf/scheduler/utm.yaml .

• Preparer: Prepare the files in the cloud storage.

  more info

  The Preparer creates the cloud file structure. It creates the needed zarr groups and arrays in order to later store the extracted patches.

  The gcp preparer config can be found in conf/preparer/gcp.yaml .

• Deployer: Deploy the extraction tasks created by the scheduler to perform the extraction.
  more info

  The Deployer sends one message per ExtractionTask to the cloud provider to perform the actal extraction. It works by publishing messages to a PubSub queue where the extraction is subscribed to. When a new message (ExtractionTask) arrives it will be automatically run on the cloud autoscaling. The gcp deployer config can be found in conf/deployer/gcp.yaml .

All the steps are optional and the user decides which to run the main config file.

In order to run SatExtractor we recommend to have a virtual env and a cloud provider user should already been created.

1. Clone the repo

   git clone https://github.com/FrontierDevelopmentLab/sat-extractor

2. Install python packages

   pip install .

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🔴🔴🔴

- WARNING!!!!:
Running SatExtractor will use your billable cloud provider services.
We strongly recommend testing it with a small region to get acquainted
with the process and have a first sense of your cloud provider costs
for the datasets you want to generate. Be sure you are running all your
cloud provider services in the same region to avoid extra costs.

🔴🔴🔴

Once a cloud provider user is set and the package is installed you'll need to grab the GeoJSON region you want (you can get it from the super-cool tool geojson.io) and change the config files.

1. Choose a region name (eg cordoba below) and create an output directory for it:

mkdir output/cordoba

2. Save the region GeoJSON as aoi.geojson and store it in the folder you just created.
3. Open the config.yaml and you'll see something like this:

dataset_name: cordoba
output: ./output/${dataset_name}

log_path: ${output}/main.log
credentials: ${output}/token.json
gpd_input: ${output}/aoi.geojson
item_collection: ${output}/item_collection.geojson
tiles: ${output}/tiles.pkl
extraction_tasks: ${output}/extraction_tasks.pkl

start_date: 2020-01-01
end_date: 2020-02-01

constellations:
  - sentinel-2
  - landsat-5
  - landsat-7
  - landsat-8

defaults:
  - stac: gcp
  - tiler: utm
  - scheduler: utm
  - deployer: gcp
  - builder: gcp
  - cloud: gcp
  - preparer: gcp
  - _self_
tasks:
  - build
  - stac
  - tile
  - schedule
  - prepare
  - deploy

hydra:
  run:
    dir: .

The important here is to set the dataset_name to <your_region_name>, define the start_date and end_date for your revisits, your constellations and the tasks to be run (you would want to run the build only one time and the comment it out.)

Important: the token.json contains the needed credentials to access you cloud provider. In this example case it contains the gcp credentials. You can see instructions for getting it below in the Authentication instructions.

3. Open the cloud/<provider>.yaml and add there your account info as in the default provided file. The storage_root must point to an existing bucket/bucket directory. user_id is simply used for naming resources. (optional): you can choose different configurations by changing modules configs: builder, stac, tiler, scheduler, preparer, etc. There you can change things like patch_size, chunk_size.

4. Run python src/satextractor/cli.py and enjoy!

See the open issues for a full list of proposed features (and known issues).

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To get the token.json for Google Cloud, the recommended approach is to create a service account:

1. Go to Credentials
2. Click Create Credentials and choose Service account
3. Enter a name (e.g. sat-extractor) and click Create and Continue
4. Under Select a role, choose Basic -> Editor and then click Done
5. Choose the account from the list and then to to the Keys tab
6. Click Add key -> Create new key -> JSON and save the file that gets downloaded
7. Rename to token.json and you're done!

For building the sat-extractor service, you may also need to configure the credentials used by the cloud provider commandline devkit. Permissions at the project-owner level are recommended. If using Google Cloud Platform, you can authorize the gcloud devkit to access Google Cloud Platform using your Google credentials by running the command gcloud auth login. You may also need to run gcloud config set project your-proj-name for sat-extractor to work properly.

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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Distributed under the BSD 2 License. See LICENSE.txt for more information.

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If you want to use this repo please cite:

@software{dorr_francisco_2021_5609657,
  author       = {Dorr, Francisco and
                  Kruitwagen, Lucas and
                  Ramos, Raúl and
                  García, Dolores and
                  Gottfriedsen, Julia and
                  Kalaitzis, Freddie},
  title        = {SatExtractor},
  month        = oct,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.1.0},
  doi          = {10.5281/zenodo.5609657},
  url          = {https://doi.org/10.5281/zenodo.5609657}
}

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This work is the result of the 2021 ESA Frontier Development Lab World Food Embeddings team. We are grateful to all organisers, mentors and sponsors for providing us this opportunity. We thank Google Cloud for providing computing and storage resources to complete this work.