Skip to main navigation Skip to search Skip to main content

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

  • Emily Fairfax
  • , Eric Zhu
  • , Nicholas Clinton
  • , Stefania Maiman
  • , Aman Shaikh
  • , William W. Macfarlane
  • , Joseph M. Wheaton
  • , Dan Ackerstein
  • , Eddie Corwin

Research output: Contribution to journalArticlepeer-review

Abstract

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. Plain Language Summary 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 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 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.

Original languageEnglish (US)
Article numbere2022JG007196
JournalJournal of Geophysical Research: Biogeosciences
Volume128
Issue number6
DOIs
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors.

Fingerprint

Dive into the research topics of 'EEAGER: A Neural Network Model for Finding Beaver Complexes in Satellite and Aerial Imagery'. Together they form a unique fingerprint.

Cite this