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The AMMonitor community is a collaboration of remote wildlife monitoring projects whose media data (audio, photos, and video and their metadata) comprise a repository for use in (1) ongoing adaptive management and research of wildlife, and (2) the development of new predictive models, via machine learning, for the automated identification of target species from media. Each collaborating project uses the AMMonitor R package and database structure for pooling data under a unifying framework that meets high quality standards for rich metadata. Each project exists as a child-item of this ScienceBase community, containing metadata about the project itself. These project items contain media folders as children, which...
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Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML...
    map background search result map search result map Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring