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Challenges and solutions for automated avian recognition in aerial imagery

Zhonqgi Miao·Stella X Yu·Kyle Lawrence Landolt·Mark D. Koneff·Timothy White·Luke J. Fara·Enrika Hlavacek·Bradley A. Pickens·Travis J. Harrison·Wayne M. Getz
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Abstract

Remote aerial sensing provides a non-invasive, large geographical-scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has 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 in the United States 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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Zhonqgi Miao, Stella X Yu, Kyle Lawrence Landolt, Mark D. Koneff, Timothy White, Luke J. Fara, Enrika Hlavacek, Bradley A. Pickens, Travis J. Harrison, Wayne M. Getz. 2023-04-26. Challenges and solutions for automated avian recognition in aerial imagery. https://doi.org/10.1002/rse2.318

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