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Challenges and Solutions for Automated Avian Recognition in Aerial Imagery
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Zhongqi Miao and Stella X. Yu and Kyle L. Landolt and Mark D. Koneff and Timothy P. White and Luke J. Fara and Enrika J. Hlavacek and Bradley A. Pickens and Travis J. Harrison and Wayne M. Getz
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Remote Sensing in Ecology and Conservation, 2023
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Paper
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Abstract
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Remote aerial sensing provides a non-invasive, large-geographical-scale technology for avian monitoring, but the manual processing of images limits its development. Artificial intelligence methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, is limited by several challenges: 1) imbalanced (i.e., long-tailed) data distribution, 2) annotation uncertainty in categorization, and 3) dataset discrepancies across different study sites. Here we use aerial imagery data of waterbirds around Cape Cod and Lake Michigan to examine how these challenges limit avian recognition performance. We review existing solutions and demonstrate as use cases how methods like Label Distribution Aware Marginal Loss with Deferred Re-Weighting, hierarchical classification, and FixMatch address the three challenges. We also present a new approach to tackle the annotation uncertainty challenge using a Soft-fine Pseudo-Label methodology. Finally, we aim with this paper to increase awareness in the ecological remote sensing community of these challenges and bridge the gap between ecological applications and state-of-the-art computer science, thereby opening new doors to future research.
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Keywords
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overhead image recognition, avian recognition, imbalanced classification, annotation uncertainty, pseudo-fine labeling
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