The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which allows benchmarking of various existing and novel AI methods tailored for EO data.
AiTLAS Introduction https://youtu.be/-3Son1NhdDg
AiTLAS Software Architecture: https://youtu.be/cLfEZFQQiXc
AiTLAS in a nutshell: https://www.youtube.com/watch?v=lhDjiZg7RwU
AiTLAS examples:
- Land use classification with multi-class classification with AiTLAS: https://youtu.be/JcJXrMch0Rc
- Land use classification with multi-label classification: https://youtu.be/yzHkEMbDW7s
- Maya archeological sites segmentation: https://youtu.be/LBFY4pCfzOU
- Visualization of learning performance: https://youtu.be/wjMfstcWBSs
The best way to install aitlas, is if you create a virtual environment and install the requirements with pip. Here are the steps:
- Go to the folder where you cloned the repo.
- Create a virtual environment
conda create -n aitlas python=3.8- Use the virtual environment
conda activate aitlas- Before installing
aitlason Windows it is recommended to install the following packages from Unofficial Windows wheels repository:
pip install GDAL-3.4.1-cp38-cp38-win_amd64.whl
pip install Fiona-1.8.20-cp38-cp38-win_amd64.whl
pip install rasterio-1.2.10-cp38-cp38-win_amd64.whl- Install the requirements
pip install -r requirements.txtAnd, that's it, you can start using aitlas!
python -m aitlas.run configs/example_config.jsonIf you want to use aitlas as a package run
pip install .in the folder where you cloned the repo.
Note: You will have to download the datasets from their respective source. You can find a link for each dataset in the respective dataset class in aitlas/datasets/ or use the AiTLAS Semantic Data Catalog
For attribution in academic contexts, please cite our work 'AiTLAS: Artificial Intelligence Toolbox for Earth Observation' published in Remote Sensing (2023) link as
@article{aitlas2023,
AUTHOR = {Dimitrovski, Ivica and Kitanovski, Ivan and Panov, Panče and Kostovska, Ana and Simidjievski, Nikola and Kocev, Dragi},
TITLE = {AiTLAS: Artificial Intelligence Toolbox for Earth Observation},
JOURNAL = {Remote Sensing},
VOLUME = {15},
YEAR = {2023},
NUMBER = {9},
ARTICLE-NUMBER = {2343},
ISSN = {2072-4292},
DOI = {10.3390/rs15092343}
}
An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, it presents a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, it employs benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks.To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are available on this repository.
repo: https://github.com/biasvariancelabs/aitlas-arena
paper: Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification , ISPRS Journal of Photogrammetry and Remote Sensing, Vol.197, pp 18-35
A novel semantic data catalog of numerous EO datasets, pertaining to various different EO and ML tasks. The catalog, that includes properties of different datasets and provides further details for their use, is available here