Machine Learning for Conservation

Machine learning has gone from a relatively niche field of academic research in the 80’s and 90’s to powering everyday services, self-driving cars, and data analyses. Since 2012, the explosion of machine learning has largely been facilitated by advances in the graphics processing units (GPUs) and the availability of massive labeled datasets.

The CBC is committed to advancing and promoting the use of machine learning for the understanding and conservation of biodiversity by:

  • Cultivating a network of conservation practitioners and machine learning experts to advance the use of machine learning for the understanding and conservation of biodiversity.
  • Developing opensource tools and workflows to facilitate the use of state of the art methods and machine learning libraries for the understanding and conservation of biodiversity.
  • Developing guides, tutorials, and workshops to introduce and demonstrate, to conservation practitioners, the utility and power of machine learning for the understanding and conservation of biodiversity.
           
Projects and initiatives:
         
Related pages:
      
Contact us!

If you have any questions about these projects, please contact Ned Horning and Peter Ersts.