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Computer Vision for Global-scale Biodiversity Monitoring -
Biodiversity is declining globally at unprecedented rates. 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. Biodiversity data is correlated in time and space, resulting in overfitting and poor generalization to new sensor deployments. Environmental monitoring sensors have limited intelligence, resulting in objects of interest that are often too close/far, blurry, or in clutter. Further, the distribution of species is long-tailed, which results in highly-imbalanced datasets. These challenges are not unique to the natural world, advances in any one of these areas will have far-reaching impact across domains. To address these challenges, we take inspiration from the value of additional contextual information for human experts, and seek to incorporate it within the structure of machine learning systems. Incorporating species distributions and temporal signal at inference time can improve generalization to new sensors without additional human data labeling, and human-AI active learning approaches can further improve these methods while keeping human labeling to a minimum. Going beyond single sensor deployment, there is a large degree of contextual information shared across multiple data streams. My long-term goal is to develop learning methods that efficiently and adaptively benefit from many different data streams in combination with targeted human expertise on a global scale.

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