Interact with our latest ML models applied to conservation problems.
A computer vision system analyzes the spot patterns of trout to identify individual fish. This innovative, non-invasive approach aims to monitor trout populations in British Columbia over time, ultimately supporting and enhancing conservation efforts in the region.
A computer vision system specifically designed to process underwater camera streams for the automatic classification and counting of wild salmon as they migrate back to their natal streams. Utilizing a robust machine learning pipeline, the system efficiently analyzes video footage, facilitating the enforcement of conservation regulations and supporting sustainable wildlife management.
An audio analysis system designed to process recordings from African forests, specifically focusing on detecting elephant rumbles. The machine learning pipeline efficiently analyzes audio files to identify and classify these distinct vocalizations, contributing to wildlife monitoring and conservation efforts.
A real-time computer vision system that analyzes camera data to monitor and detect forest fires. By leveraging advanced image processing techniques, the system aims to provide timely alerts and insights, enhancing fire management and response efforts.
A computer vision system detects and deters bears from encroaching on Romanian farms, contributing to the harmonious coexistence of farmers and wildlife, including predators like bears.
A computer vision system utilizes facial recognition technology to analyze bear photographs and identify individual bears. This innovative system aims to monitor the population size of bears in British Columbia over time, ultimately supporting and enhancing conservation efforts in the region.
A computer vision system analyzes benthic imagery data to detect and identify various coral species. Utilizing advanced image processing techniques, the system is designed to monitor the health of coral reefs over time, thereby supporting and enhancing conservation efforts.
