bird-cloud-gnn
BI
bird-cloud-gnn is a Python package that helps generate a graph representation from point cloud data and fits graph neural networks for classification problem
Updated 4 months ago
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Birds flying at around 1 km altitude captured by radar in Florida (image credit: National Weather Service)
The goal of this project was to improve the extraction of bird and insect movement data from meteorological radars by retaining spatial details of radar echoes, which is often lost in existing methods. This biodiversity information is crucial for monitoring species, disease spread, aviation safety, and agriculture. Typical image-based tools struggled with the complexity of radar data, and while point-cloud methods were explored, they proved computationally infeasible. We introduced graph-based classifiers, which maintain the spatial structure of radar data and significantly enhance the classification process.
The project offers a novel approach for the research community, improving accuracy and efficiency in classifying biological radar echoes. We achieved a classification accuracy of 90%, overcoming challenges like a shortage of labeled data by converting point cloud data into graph representations. This was validated through rigorous cross-validation techniques.
The target audience for our results includes environmental monitoring agencies, and researchers in biodiversity and meteorology. Our next steps involve refining the tool and exploring broader applications. For more information or to explore collaboration, please contact us.