aviary provides composable components for building inference and postprocessing pipelines
for remote sensing data.
This enables you to easily run models on large datasets, export the predictions in a
georeferenced file format and postprocess them for further downstream tasks.
Besides the pipelines, aviary also provides task-specific models for remote sensing applications.
aviary is designed upon the following concepts:
-
High-level Python API
Abstract components for building pipelines without boilerplate code -
Command-line interface (CLI)
Run the pre-built pipelines easily without writing any code -
Customizable pipelines
Compose your own pipelines with the provided components -
Extensible components
Add your own components to the pipeline -
Support for large datasets
Tile-based processing for large datasets (local, remote or web services) -
Support for geospatial data
Export predictions as geodata, ready for downstream tasks
You can choose between two installation methods, whether you need access to the Python API or the command-line interface (CLI) only. If you just want to use the pre-built pipelines with the command-line interface, you can use the Docker image.
pip install geospaitial-lab-aviary
Note that aviary requires Python 3.10 or later.
Have a look at the installation guide for further information.
uv pip install geospaitial-lab-aviary
Note that aviary requires Python 3.10 or later.
Have a look at the installation guide for further information.
docker pull ghcr.io/geospaitial-lab/aviary
Have a look at the installation guide for further information.
Have a look at the how-to guides to get started.
The full documentation is available at geospaitial-lab.github.io/aviary.
aviary is developed by the geospaitial lab at the Westfälische Hochschule - Westphalian University of Applied Sciences in Gelsenkirchen, Germany.