Where the Wild Things Are: Computer Vision for Global-Scale Biodiversity Monitoring

Citation

Beery, Sara Meghan (2023) Where the Wild Things Are: Computer Vision for Global-Scale Biodiversity Monitoring. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/m4mt-2q51. https://resolver.caltech.edu/CaltechTHESIS:04242022-005200355

Abstract

We require a real-time, modular earth observation system that unites efforts across research groups in order to provide the necessary information necessary for global-scale impact in sustainability and conservation in the face of climate change. The development of such systems requires collaborative, interdisciplinary approaches that translate diverse sources of raw information into accessible scientific insight. For example, we need to monitor species in real time and in greater detail to quickly understand which conservation efforts are most effective and take corrective action. Current ecological monitoring systems generate data far faster than researchers can analyze it, making scaling up impossible without automated data processing. However, ecological data collected in the field presents a number of challenges that current methods, like deep learning, are not designed to tackle. These include strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. Our work seeks to overcome these challenges, and this thesis includes methods which can learn from imperfect data, systematic frameworks and benchmarks for measuring and overcoming performance drops due to domain shift, and the development and deployment of efficient human-AI systems that have made significant real-world conservation impact.

Thesis Files