Summary
Collecting data about animals often creates more information than experts can analyze because it requires careful labeling. The authors look at a method called active learning that helps decide which data samples experts should label to teach computers better, using less expert time. However, they highlight that how experts spend their limited time also affects how reliable the computer’s predictions and ecological studies are. They review current research, find many gaps such as ignoring some animal groups and ways to check results, and offer guidance on making active learning work well for real biodiversity efforts.
What this means in practice
- •For wildlife monitoring teams: Manage expert annotation time more effectively to ensure both accurate model training and trustworthy ecological estimates from biodiversity data.
- •For audio and image data engineers: Develop active learning tools that integrate model validation and ecological inference needs for real-world animal monitoring projects.
A survey. It maps existing work.
Abstract
Limited expert annotation capacity is a pervasive constraint in biodiversity monitoring. Passive acoustic recorders and camera traps generate data faster than experts can analyse them. Machine learning (ML) models can process these data at scale, but their reliability depends on the quality, quantity, and coverage of labelled samples, so expert time remains a constraint. Active learning (AL) eases this bottleneck by selecting, under a fixed annotation budget, the samples expected to improve a model most, and published evidence shows it can reduce the labels needed to reach a target performance. Monitoring programmes, however, face a broader question: how should a limited expert budget be divided so that model training, validation, and the ecological estimates built on model outputs all remain reliable? Because AL selects samples non-randomly, its labels are unsuitable for validation, calibration, or threshold selection, a tension rarely acknowledged. We synthesise AL research across acoustic and image modalities and identify gaps and opportunities. Most studies evaluate query strategies on pre-labelled benchmarks with simulated annotators; deployments in real monitoring workflows are rare and concentrate on birds and cetaceans. Bats, insects, amphibians, and fish are underrepresented, and multimodal applications remain largely unexplored. Evaluation centres on headline reductions in annotation effort, often without random-sampling baselines, per-class results, or calibration analysis, and rarely accounts for the labels required for validation. We provide a tutorial treatment of the AL loop that makes these budget decisions explicit, and a roadmap towards AL methods that support label-efficient training, validation, and trustworthy downstream ecological inference.