Author Results: N. Clinton

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EEAGER: A Neural Network Model for Finding Beaver Complexes in Satellite and Aerial Imagery

Beavers are ecosystem engineers that create and maintain riparian wetland ecosystems in a variety of ecologic, climatic, and physical settings. Despite the large-scale implications of ongoing beaver conservation and range expansion, relatively few landscape-scale studies have been conducted, due in part to the significant time required to manually locate beaver dams at scale. To address this need, we developed EEAGER—an image recognition machine learning model that detects beaver complexes in aerial and satellite imagery. We developed the model in the western United States using 13,344 known beaver dam locations and 56,728 nearby locations without beaver dams. Performance assessment was performed in twelve held out evaluation polygons of known beaver occupancy but previously unmapped dam locations. These polygons represented regions similar to the training data as well as more novel landscape settings. Our model performed well overall (accuracy = 98.5%, recall = 63.03%, precision = 25.83%) in these areas, with stronger performance in regions similar to where the model had been trained. We favored recall over precision, which results in a more complete catalog of beaver dams found but also a higher incidence of false positives to be manually removed during quality control. These results have far-reaching implications for monitoring of beaver-based river restoration, as well as potential applications detecting other complex landforms.

EEAGER: a model that detects beaver-created wetlands in satellite and aerial imagery

Beavers are ecosystem engineers that can dramatically change the shape of the landscape and how water moves through it. They create and maintain wetland environments across North America and Eurasia in a wide variety of places, including mountains, deserts, coasts, forests, grasslands, shrublands, etc. Despite their large influence on the landscape, there are very few programs that monitor them at the landscape, regional, or continental scale. This is partially due to how much time it takes to find and identify beaver dams in satellite and aerial images. To make it easier for us to find and understand the influence of beavers at larger scales, we built a model that can automatically find beaver dams in satellite and aerial imagery. While our model is trained to find beaver dams, this type of model has promise for finding other landscape features too. The model isn’t perfect, but it is a strong starting point and will continue to improve as more people use it.