Automated Ecological Monitoring - Learning from Context
Profile
Automated Ecological Monitoring - Learning from Context
PI: Pietro Perona
Research Team: Sara Beery and Elijah Cole
Division of Engineering and Applied Science
Climate Science and Ecology and Biosphere Engineering Initiatives
2020 Explorer grant
Accurate biodiversity monitoring is essential for the development of new sustainability policies, guidelines, and strategies, and requires automation of the analysis of large image and sound data sets. This proposal aims to develop new machine learning frameworks from multiple data streams, and will help to improve accuracy, spatial, and temporal resolution of the biodiversity monitoring projects.