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


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.


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.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Computer Vision, Machine Learning, Data Science, Biodiversity Monitoring, Conservation Technology
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computing and Mathematical Sciences
Awards:Amori Doctoral Prize in CMS, 2023. Amazon AI4Science Fellowship. PIMCO Data Science Fellowship. Caltech CMS Gradient for Change Award, 2021. Caltech EAS New Horizons Award.
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Perona, Pietro
Thesis Committee:
  • Yue, Yisong (chair)
  • Bouman, Katherine L.
  • Belongie, Serge J.
  • Perona, Pietro
Defense Date:20 May 2022
Funding AgencyGrant Number
National Science Foundation Graduate Research Fellowship1745301
Resnick Sustainability InstituteUNSPECIFIED
Record Number:CaltechTHESIS:04242022-005200355
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Chapter 1 adapted for Chapter 2 adapted for Chapter 3 adapted for Chapter 4 adapted for Chapter 5 adapted for Chapter 5 adapted for Chapter 5 adapted for Chapter 5 adapted for Chapter 5 adapted for Chapter 5 adapted for Chapter 6 adapted for Chapter 7 adapted for Chapter 8 adapted for Chapter 9 adapted for Chapter 10
Beery, Sara Meghan0000-0002-2544-1844
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14556
Deposited By: Sara Beery
Deposited On:17 Feb 2023 17:45
Last Modified:08 Nov 2023 00:44

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