arXiv Machine Learning
Sep 24

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

The paper discusses how active learning (AL) can alleviate the expert annotation bottleneck in biodiversity monitoring by selecting the most informative samples under a fixed budget. It highlights that while AL reduces labeling effort, its non-random sample selection complicates model validation, calibration, and ecological inference, issues often overlooked in current studies. The authors review existing AL research across acoustic and image data, identify gaps such as limited species coverage and lack of real-world deployments, and propose a tutorial framework and roadmap for developing AL methods that support efficient training, reliable validation, and trustworthy ecological conclusions.

By Ben McEwen, Shiqi Zhang, Dan Stowell
arXiv Machine Learning
Sep 23

Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling

The paper introduces ACORN, a method that blends machine‑learning predictions with occupancy models to guide ecologists in selecting which samples to review. By strategically choosing the most informative labels, ACORN achieves ecological conclusions nearly identical to fully human‑labeled data while dramatically reducing the number of expert reviews needed. The approach is evaluated on camera‑trap and bioacoustic datasets, demonstrating its effectiveness across real‑world biodiversity surveys.

By Timm Haucke, Lauren Harrell, Justin Kay, Mary Clapp, Sara Beery
arXiv AI
Jun 12

GetNetUPAM: Ecologically Informed Nested Cross-Validation and Noise-Robust Attention for Marine Bioacoustic Monitoring

arXiv:2509. 04682v2 Announce Type: replace-cross Abstract: Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice.

By Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, KC Santosh