Minerva is a package to aid in the building, fitting and testing of neural network models on multi-spectral geo-spatial data.
If one wishes to use torchgeo, installation on Linux is recommended to handle the
compilation of the required C-based libraries -- though minerva is also tested with MacOS and Windows runners.
minerva is currently not included in any distribution. The recommended install is therefore to install the latest version from GitHub.
pip install git+https://github.com/Pale-Blue-Dot-97/Minerva.gitminerva now supports the use of torchgeo
datasets with upcoming support for torchvision datasets.
Required Python modules for minerva are stated in the setup.cfg.
minerva currently only supports python 3.9 -- 3.11.
The core functionality of minerva provides the modules to define models to fit and test, loaders to pre-process,
load and parse data, and a Trainer to handle all aspects of a model fitting. Below is a MWE of creating datasets,
initialising a Trainer and model, and fitting and testing that model then outputting the results:
from minerva.utils import CONFIG # Module containing various utility functions.
from minerva.trainer import Trainer # Class designed to handle fitting of model.
# Initialise a Trainer. Also creates the model.
trainer = Trainer(**CONFIG)
# Run the fitting (train and validation epochs).
trainer.fit()
# Run the testing epoch and output results.
trainer.test()See scripts\MinervaExp.py as an example script implementing minerva.
See minerva\inbuilt_cfgs\example_config.yml as an example config file.
Use scripts\ManifestMake.py to construct a manifest to act as a look-up table for a dataset.
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!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Minerva is distributed under a MIT License.
Created by Harry Baker as part of a project towards for a PhD in Computer Science from the University of Southampton. Funded by the Ordnance Survey Ltd.
Contributions also provided by:
I'd like to acknowledge the invaluable supervision and contributions of Prof Jonathon Hare and Dr Isabel Sargent towards this work.
The following modules are adapted from open source third-parites:
| Module | Original Author | License | Link |
|---|---|---|---|
pytorchtools |
Noah Golmant | MIT | lars |
optimisers |
Bjarte Mehus Sunde | MIT | early-stopping-pytorch |
dfc |
Lukas Liebel | GNU GPL v3.0 | dfc2020_baseline |
This repositry also contains some small samples from various public datasets for unit testing purposes. These are:
| Dataset | Citation | License | Link |
|---|---|---|---|
| ChesapeakeCVPR | Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N, "Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data". Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019) | Unknown | ChesapeakeCVPR |
| SSL4EO-S12 | Wang Y, Braham N A A, Xiong Z, Liu C, Albrecht C M, Zhu X X, "SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation". arXiv preprint, 2023 | Apache 2.0 | SSL4E0-S12 |
| DFC2020 | M. Schmitt, L. H. Hughes, C. Qiu, and X. X. Zhu, “SEN12MS – A curated dataset of georeferenced multi-spectral sentinel-1/2 imagery for deep learning and data fusion,” in ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. IV-2/W7, 2019, pp. 153–160. | Creative Commons Attribution | IEEE DFC2020 |
This project is now in release beta state. Still expect some bugs and there may be breaking changes in future versions.