Automated visitor and wildlife monitoring with camera traps and machine learning

Veronika Mitterwallner, Anne Peters, Hendrik Edelhoff, Gregor Mathes, Hien Nguyen, Wibke Peters, Marco Heurich, Manuel J. Steinbauer

Remote Sensing in Ecology and Conservation 2023 article cited by 42

Source Open-access PDF More in Climate & Environment

importance 4/10 ai assisted Wildlife Ecology and Conservation wildlife monitoring stage: research open-source model

Problem

Manually classifying hundreds of thousands of camera trap images to study human–wildlife interactions is slow and labor-intensive.

Why it matters

Understanding human–wildlife interactions is crucial as human activity in natural areas increases.

What the AI did

Evaluated the open-source object detection model MegaDetector for detecting and counting humans, animals and vehicles, compared against manual classification of more than 300,000 images from three regions.

Results

not stated in source

Impact

Provides a large-scale cross-regional evaluation of MegaDetector for ecological camera trap monitoring.

Record

Venue	Remote Sensing in Ecology and Conservation
Published	2023
Work type	article
Authors	Veronika Mitterwallner, Anne Peters, Hendrik Edelhoff, Gregor Mathes, Hien Nguyen, Wibke Peters, Marco Heurich, Manuel J. Steinbauer
Citations	42 (OpenAlex, at import time)
References	41
Model or method	MegaDetector
Organization	not stated in source
Primary source	https://doi.org/10.1002/rse2.367
Open access	gold
Keywords	camera traps, MegaDetector, object detection, wildlife monitoring, ecology

Importance rating

4 / 10

real but narrower advance

Abstract states understanding human-wildlife interactions is crucial as human activities in natural areas increase; large-scale automated image processing aids ecological research — a real but narrower methodological advance.

Basis: stated. The source itself states the scale. Stated scale: more than 300,000 camera trap images across three study regions. Method and limits.

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