The MillionTrees benchmark is designed to provie open, reproducible and rigorous evaluation of tree detection algorithms. The dataset currently holds X images, X annotations and X train/test evaluation splits. This repo is the python package for rapid data sharing and evaluation. We anticipate the dataset will be ready by spring 2025.
There are 3 datasets available for the MillionTrees benchmark:
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TreeBoxes: A dataset of 282,288 tree crowns from 9 sources.
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TreePolygons: A dataset of 362,751 tree crowns from 8 sources.
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TreePoints: A dataset of 191,614 tree stems from 2 sources.
There has been a tremendous number of tree crown detection benchmarks, but a lack of progress towards a single algorithm that can be used globally across aquisition sensors, forest type and annotation geometry. Our view is that the hundreds of tree detection algorithms for RGB data published in the last 10 years are all data starved. There are many good models, but they can only be so useful with the small datasets any research team can collect. The result is years of effort in model development, but ultimately a lacking solution for a large audience. The MillionTrees dataset seeks to collect a million annotations across point, polygon and box geometries at a global scale.
pip install MillionTrees
To build from the GitHub source and install the required dependencies, follow these instructions:
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Clone the GitHub repository:
git clone https://github.com/username/repo.git -
Change to the repository directory:
cd repo -
Install the required dependencies using pip:
pip install -r requirements.txt -
(Optional) Build and install the package:
python setup.py install
Once the installation is complete, you can use the MillionTrees package in your Python projects.
The MillionTrees package has ingested many contributed datasets and formatted them in a consistant fashion.
| Geometry | Training Images | Training Annotations | Eval Images | Eval Annotations |
|---|---|---|---|---|
| Point | ||||
| Polygon | ||||
| Box |
The design of the MillionTrees benchmark was inspired by the WILDS benchmark, and we are grateful to their work, as well as Sara Beery for suggesting the use of this template.