Towards AI-Guided Open-World Ecological Taxonomic Classification

2025-12-21

AI Summary

Addressing four key challenges in open-world ecological classification—long-tailed class distribution, fine-grained intra-class variation, spatiotemporal domain shifts, and the restrictive closed-set assumption—this work proposes TaxoNet, a novel encoder architecture coupled with a dual-boundary penalty loss. TaxoNet unifies modeling of multi-source ecological uncertainties for the first time, enabling reliable rejection of unknown taxa and robust recognition of rare classes beyond the closed-set paradigm. Leveraging an embedded encoding design and bilateral margin contrastive learning, it achieves cross-domain adaptation across Google Auto-Arborist, iNat-Plantae, and NAFlora-Mini. Evaluated on three ecological benchmarks, TaxoNet significantly outperforms state-of-the-art baselines, boosting rare-class accuracy by up to 32.7%. Furthermore, our analysis reveals critical domain adaptation bottlenecks of general-purpose multimodal large language models in plant taxonomy, establishing a new paradigm for sustainable biodiversity monitoring.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Addresses long-tailed taxonomic distributions and class imbalance
• Tackles fine-grained variations and spatiotemporal domain shifts
• Overcomes closed-set assumptions for open-world ecological classification

Innovation

• Open-world framework for ecological taxonomy classification
• Dual-margin penalization loss addressing class imbalance and rare taxa
• Embedding-based encoder improving performance across diverse ecological domains