Automatic Looting Classification from Earth Observation (ALCEO)

This is the source code repository of the Python module developed for the ALCEO project. Reference of the Program-Contract: ALCEO Grant n. 4000136771, between European Space Agency (ESA) and Istituto Italiano di Tecnologia (IIT) for ALCEO: Automatic Looting Classification from Earth Observation.

Documentation website can be found at: https://iit-ccht.github.io/alceo/

Installing the ALCEO Python module

The first step to install the alceo python module is to clone the repository. This project uses Poetry as a dependency manager.
You can use poetry install to download all the dependencies and have the alceo module installed.
The [tool.poetry.dependencies] section of pyproject.toml metadata file contains the project dependencies definition.
By default, Poetry creates a virtual environment for the project. Refer to Poetry's documentation to configure this behavior.

Serving the documentation locally

To host the documentation website on the local machine, use the mkdocs serve command after installing all the project dependencies.

Retrieving Data and Models from a DVC Remote

Data Version Control (DVC) has been extensively used in this project to create reproducible pipelines for handling data pre-processing, training, validating, testing, and performing inference of the machine learning models implemented during the project.

A DVC remote is needed for downloading and updating all experiments' data, logged metrics, and model checkpoints. As explained in DVC's documentation, one can add a new (default) remote by using the following command:

dvc remote add -d myremote /path/to/remote

Consult DVC documentation for more information about configuring remotes and a list of supported storage types.

If the configured remote already contains project data, the dvc pull command will download them locally.

Running a pipeline

The Data Management sub-system and Modelling sub-system pipelines are defined using DVC, for e detailed description of these sub-systems, refer to the corresponding sections in the documentation navigation menu.

The definition of the DVC pipeline stages happens inside various dvc.yaml files.
To reproduce all the pipelines use the optional argument -P (or --all-pipelines).
Specific stage targets can be selected for reproduction dvc repro [targets [targets ...]]. To run a stage provide to the dvc repro command the path to the dvc.yaml file followed by colon : and the stage name. For example, from the project root:

dvc repro pipelines/fit/dvc.yaml:fit_siam_diff