This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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Abstract
Monitoring wildlife is crucial for making informed conservation and land-management decisions. Remotely triggered cameras are widely used for this, but the resulting 'big data' are laborious to process. Artificial intelligence (AI) offers a solution to this bottleneck, but it has been challenging for ecologists and practitioners to tailor current approaches to their specific use cases. Generic, online offerings also have issues of ongoing costs and data privacy. Here we present an open-source, scalable, modular, cross-platform workflow, deployed using Docker, which leverages deep learning for wildlife-image classification. Run via a user-friendly command-line interface, our workflow democratises the implementation of AI for wildlife-image classification enabling end-users without specialised technical expertise to execute a full range of tasks—from animal detection to species prediction—on local or cloud GPU-accelerated machines. It integrates seamlessly with the widely used open-source camera-trapping software ‘Camelot’, writing AI-classification data directly to image metadata and to CSV files, ready for either expert verification or direct data analysis. The end result is an advanced but accessible pipeline for wildlife-image classification. A case study with Tasmanian wildlife demonstrates the utility of our end-to-end pipeline, from classifier training to inference.
DOI
https://doi.org/10.32942/X2ZW3D
Subjects
Biodiversity, Ecology and Evolutionary Biology, Life Sciences, Zoology
Keywords
Wildlife monitoring, Artificial Intelligence, image classification, Deep learning, camera trapping, open-source software, Ecological Data Processing, docker, Camelot, MegaDetector
Dates
Published: 2023-12-11 21:32
Last Updated: 2023-12-12 05:32
License
CC-BY Attribution-NonCommercial 4.0 International
Additional Metadata
Language:
English
Conflict of interest statement:
None
Data and Code Availability Statement:
Code (open source): http://github.com/zaandahl/mewc; Data: Brook, B, Buettel, J (2023) Mega-Efficient Wildlife Classifier (MEWC) Case Study. https://dx.doi.org/10.25959/wm5g-b990
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