Deep learning for wildlife conservation and restoration efforts

Climate change and environmental degradation are causing species extinction worldwide. Automatic wildlife sensing is an urgent requirement to track biodiversity losses on Earth. Recent improvements in machine learning can accelerate the development of large-scale monitoring systems that would help track conservation outcomes and target efforts. In this paper, we present one such system we developed. ’Tidzam’ is a Deep Learning framework for wildlife detection, identification, and geolocalization, designed for the Tidmarsh Wildlife Sanctuary, the site of the largest freshwater wetland restoration in Massachusetts.