biasvariancelabs / aitlas
AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.
Why this job: matched by what the repo talks about: image, art, method, implement
What it does
- New build system. Complete migration to uv and pyproject.toml for faster, reproducible builds.
- Modern infrastructure. Switched to ruff for ultra-fast linting and formatting.
- Foundation model architecture. Implemented CompositeModel, allowing for dynamic building of backbones, necks, decoders, heads, and data-model adapters.
- Training. Added Automatic Mixed Precision (AMP), early stopping on NaN loss, and state preservation (LR scheduler/checkpoints) for restarts.
- Adapters. Implemented specific data-model adapters for foundation models, such as Terramind, AnySat, Galileo, and Panopticon.
Get started
uv pip install aitlas- Maintenance
- Active
- Last commit 4 months ago
- Stars
- 232
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v2.0.0
- Released 4 months ago
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- 0
- Last 90 days
- Issues
- 71% closed
- 2 still open
- Pull requests
- 15 merged
- 4 waiting
- Releases
- 1
- 1 in the last year
- No contributing guide
- No security policy
- No code of conduct
More: languages, topics and project record
Languages
- Python 100%
- C++ <1%
- Cuda <1%
- Shell <1%
Topics
- artificial-intelligence
- classification
- deep-learning
- earth-observation
- machine-learning
- remote-sensing
- dataset
- deep-neural-networks
- image-classification
- object-detection
- pytorch
- satellite-data
Project record
- JobImage generation
- StatusListed
- Commits845
- Forks43
- Watching9
- Stars in 28 days0
- CreatedMay 15, 2020
- First seen by usNov 7, 2026
- CheckedNov 12, 2026