# Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference

arXiv: https://arxiv.org/abs/2609.27409 · HTML: https://mlsanity.com/paper/2609.27409/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 48 of its 95 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 30 of 48)

- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [BioCLIP: A Vision Foundation Model for the Tree of Life](https://mlsanity.com/paper/2311.18803/) (2023)
- [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://mlsanity.com/paper/2202.02794/) (2022)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [Active Testing: Sample-Efficient Model Evaluation](https://mlsanity.com/paper/2103.05331/) (2021)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [On Statistical Bias In Active Learning: How and When To Fix It](https://mlsanity.com/paper/2101.11665/) (2021)
- [Practical Obstacles to Deploying Active Learning](https://mlsanity.com/paper/1807.04801/) (2018)
- [AVES: Animal Vocalization Encoder based on Self-Supervision](https://mlsanity.com/paper/2210.14493/) (2022)
- [Global birdsong embeddings enable superior transfer learning for bioacoustic classification](https://mlsanity.com/paper/2307.06292/) (2023)
- [A deep active learning system for species identification and counting in camera trap images](https://mlsanity.com/paper/1910.09716/) (2019)
- [Parting with Illusions about Deep Active Learning](https://mlsanity.com/paper/1912.05361/) (2019)
- [Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm](https://mlsanity.com/paper/2303.14382/) (2023)
- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [modAL: A modular active learning framework for Python](https://mlsanity.com/paper/1805.00979/) (2018)
- [Active Learning Helps Pretrained Models Learn the Intended Task](https://mlsanity.com/paper/2204.08491/) (2022)
- [Can Masked Autoencoders Also Listen to Birds?](https://mlsanity.com/paper/2504.12880/) (2025)
- [Iterative Human and Automated Identification of Wildlife Images](https://mlsanity.com/paper/2105.02320/) (2021)
- [Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation](https://mlsanity.com/paper/2405.12930/) (2024)
- [animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics](https://mlsanity.com/paper/2406.01253/) (2024)
- [DeepAL: Deep Active Learning in Python](https://mlsanity.com/paper/2111.15258/) (2021)
- [Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images](https://mlsanity.com/paper/2311.01064/) (2023)
- [Active Learning-Based Species Range Estimation](https://mlsanity.com/paper/2311.02061/) (2023)
- [Automated wildlife image classification: An active learning tool for ecological applications](https://mlsanity.com/paper/2303.15823/) (2023)
- [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025)
- [AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring](https://mlsanity.com/paper/2307.06860/) (2023)
- [Towards Deep Active Learning in Avian Bioacoustics](https://mlsanity.com/paper/2406.18621/) (2024)
- [Active and Incremental Learning with Weak Supervision](https://mlsanity.com/paper/2001.07100/) (2020)

## Abstract (arXiv)

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.