Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data

Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision–language models into these workflows could address this gap by providing enhanced...

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Bibliographic Details
Main Authors: Paul Fergus, Carl Chalmers, Naomi Matthews, Stuart Nixon, André Burger, Oliver Hartley, Chris Sutherland, Xavier Lambin, Steven Longmore, Serge Wich
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
wildlife conservation
deep learning
object detection
large language models
vision transformers
biodiversity monitoring
Online Access:https://www.mdpi.com/1424-8220/24/24/8122
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