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This volume's release consists of 64642 media files captured by autonomous wildlife monitoring devices under the project, Maine Department of Inland Fisheries and Wildlife. The attached files listed below include several CSV files that provide information about the data release. The file, "media.csv" provides the metadata about the media, such as filename and date/time of capture. The actual media files are housed within folders under the volume's "child items" as compressed files. A critical CSV file is "dictionary.csv", which describes each CSV file, including field names, data types, descriptions, and the relationship of each field to fields other CSV files. Some of the media files may have been "tagged" or "annotated"...
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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 Maine Department of Inland Fisheries and Wildlife Volume 1 (2022 - 2023) Maine Department of Inland Fisheries and Wildlife Volume 1 (2022 - 2023) Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring