Towards AI-Guided Open-World Ecological Taxonomic Classification
This work addresses the problem of open-world taxonomic classification for ecological monitoring and conservation, though it appears incremental as it builds on existing embedding-based methods.
The paper tackled the problem of AI-guided ecological taxonomic classification by addressing challenges like class imbalance, fine-grained variations, and domain shifts, introducing the Open-World Ecological Taxonomy Classification framework and TaxoNet, which outperformed baselines, especially for rare taxa, on datasets like Google Auto-Arborist and iNat-Plantae.
AI-guided classification of ecological families, genera, and species underpins global sustainability efforts such as biodiversity monitoring, conservation planning, and policy-making. Progress toward this goal is hindered by long-tailed taxonomic distributions from class imbalance, along with fine-grained taxonomic variations, test-time spatiotemporal domain shifts, and closed-set assumptions that can only recognize previously seen taxa. We introduce the Open-World Ecological Taxonomy Classification, a unified framework that captures the co-occurrence of these challenges in realistic ecological settings. To address them, we propose TaxoNet, an embedding-based encoder with a dual-margin penalization loss that strengthens learning signals from rare underrepresented taxa while mitigating the dominance of overrepresented ones, directly confronting interrelated challenges. We evaluate our method on diverse ecological domains: Google Auto-Arborist (urban trees), iNat-Plantae (Plantae observations from various ecosystems in iNaturalist-2019), and NAFlora-Mini (a curated herbarium collection). Our model consistently outperforms baselines, particularly for rare taxa, establishing a strong foundation for open-world plant taxonomic monitoring. Our findings further show that general-purpose multimodal foundation models remain constrained in plant-domain applications.