This repository accompanies the paper AutoMergeNet: AutoML-based M-Source Satellite Data Fusion Evaluated with Atmospheric Case Studies

The code is structured as follows:

• The automergenet folder contains a pip-installable package with code for:
  • Training
  • Model architectures
  • Data loading (for carbon monoxide and methane)
  • Experiment utility
• The config folder contains configuration files for the experiments
• The scripts folder contains scripts for:
  • Running and evaluating NAS (nas_search.py)
  • Preparing/preprocessing methane data (create_methane_dataset.py).
• The env.yaml file contains the conda packages used in the experiments

This project runs with an MIT license. Please see the LICENSE file for more details.

The paths to the datasets are optional arguments in the Dataset class definitions that default to "data/<dataset_name>". The datasets are not included in this repository. The datasets used in the experiments are:

• Carbon monoxide
• Methane

1. Download the methane data
2. Process the methane data using the create_methane_dataset.py script.
3. Download the carbon monoxide data
4. Normalise the carbon monoxide data
5. Update the paths to datasets in the Dataset class definitions (multi_modal_data.py and co_data.py)
6. Update save paths in the configuration files
7. Run the experiments using the scripts in the scripts folder

Please cite the following paper if you use this code:

J. Wąsala et al., "AutoMergeNet: AutoML-based M-Source Satellite Data Fusion Evaluated with Atmospheric Case Studies," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2025.3621068.