From pixels to conservation: Deep learning for automated monitoring of rare and endangered wildlife

Donghua Xuan; Yuchen Cai; Minxi Xu; Chao Long; Nian Cai

doi:10.1016/j.isci.2026.116669

What is it about?

This review systematically analyzes 159 wildlife monitoring studies (2014-2026) across four technical domains: species recognition, individual re-identification, soft biometrics (age/sex estimation), and behavior/pose analysis.

Why is it important?

Conservation of rare and endangered species requires scalable monitoring solutions to address accelerating biodiversity loss. Deep learning has transformed wildlife monitoring, enabling automated species identification, individual tracking, demographic inference, and behavioral analysis from camera traps, drones, and video surveillance.

This page is a summary of: From pixels to conservation: Deep learning for automated monitoring of rare and endangered wildlife, iScience, October 2026, Elsevier,
DOI: 10.1016/j.isci.2026.116669.
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From Pixels to Conservation: Deep Learning for Automated Monitoring of Rare and Endangered Wildlife
This is the published paper.

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Dr. Nian Cai
Guangdong University of Technology

From Pixels to Wildlife Conservation

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Why is it important?

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