sams-tom/Multimodal-AUV
Leveraging Bayesian Neural Networks for multimodal AUV data fusion, enabling precise and uncertainty-aware mapping of underwater environments.
This project helps marine scientists and environmental managers create highly accurate, uncertainty-aware maps of underwater habitats. It takes raw data from Autonomous Underwater Vehicles (AUVs), including multibeam sonar, side-scan sonar, and optical imagery, to produce detailed classifications of seafloor types like sand, mud, or kelp forests. This allows for more reliable decision-making in marine conservation, resource management, and autonomous navigation.
Available on PyPI.
Use this if you need to map complex underwater environments with high precision and require a clear understanding of the confidence level for each habitat prediction.
Not ideal if you only need basic, qualitative underwater surveys or do not have access to diverse AUV sensor data (sonar and imagery).
Stars
8
Forks
—
Language
Python
License
MIT
Category
Last pushed
Oct 24, 2025
Commits (30d)
0
Dependencies
23
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