This paper relies on a novel participatory approach, enlisting researchers, conservation practitioners, and nature enthusiasts to create a unique crowd-sourced, open-access dataset of acoustic annotations across taxonomic groups for biodiversity in India.
The results show that MAST can effectively extend self-supervised audio representations from clip-level tasks to robust box-level localization across diverse bioacoustic settings, providing a practical path for biodiversity monitoring with limited labels.
Tian-Yi Xu, Daniel L. Pimentel-Alarcón, Z. Buřivalová et al.· 1 citation
WIO-ReefFish, a reef fish detection dataset derived from diver-operated line-intercept transects and designed for ecological monitoring under natural survey conditions, is presented and established as a realistic benchmark for automated reef fish detection and a foundation for more robust computer-vision tools in coral...
J. Gerard, Luca Branger, F. Huyghe et al.· bioRxiv· 0 citations
Forest restoration has become a widespread response to deforestation and biodiversity loss, particularly across the tropics. Monitoring restoration is essential for tracking progress and requires accurate knowledge of baseline communities, with threatened species often used as recovery indicators. Passive Acoustic Moni...
Heather X. Fortune, Rhys Preston-Allen, Xiu-Han Zhang et al.· bioRxiv· 0 citations
Passive acoustic monitoring is an important tool for biodiversity assessment and wildlife conservation because it supports continuous and non-invasive monitoring of species across large spatial and temporal scales. Robust monitoring remains challenging because many datasets contain sparse positive labels, where species...
This mini-review synthesizes advances in AI-based wildlife monitoring across image and audio modalities, focusing on generalization, data imbalance, and deployment on resource-constrained devices.
Delia Velasco-Montero, J. Fernández-Berni, R. Sankaran et al.· Frontiers in Conservation Sc...· 0 citations
This paper presents a manually curated, long-tailed dataset of around one million images of insects, extracted from 1,801 camera-trap video recordings and annotated with a five-level, 34-class hierarchy, and adjusts a hierarchical classification model architecture to a five-level variable-depth hierarchy, with class-ba...
Zaki Mahfoud, Juan A. Chiavassa, Simon Walther et al.· arXiv.org· 0 citations
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