Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring
Description
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 model’s performance on its ability to accelerate human labeling. Six participants tagged trail camera images collected from 12 sites in Vermont and Maine, USA (January-September 2022) using three tagging methods (one with ML bounding box assistance and two without assistance).
Resources
| Name |
Format |
Description |
Link |
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55 |
Landing page for access to the data |
https://doi.org/10.5066/P9FGUQEZ |
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55 |
The metadata original format |
https://data.usgs.gov/datacatalog/metadata/USGS.64da3a38d34ef477cf3edf0e.xml |
Tags
- biota
- usgs-64da3a38d34ef477cf3edf0e
- vermont
- maine
- data-labelling
- trail-camera
- camera-trap
- wildlife-monitoring
- tagging
- machine-learning