Physical Sciences and Mathematics Commons^™

Nabat Ml: Utilizing Deep Learning To Enable Crowdsourced Development Of Automated, Scalable Solutions For Documenting North American Bat Populations, Ali Khalighifar, Benjamin S. Gotthold, Erin Adams, Jenny Barnett, Laura O. Beard, Eric R. Britzke, Paul A. Burger, Kimberly Chase, Zackary Cordes, Paul M. Cryan, Emily Emily, Christopher T. Fill, Scott E. Gibson, G. Scott Haulton, Kathryn M. Irvine, Lara S. Katz, William L. Kendall, Christen A. Long, Oisin Mac Aodha, Tessa Mcburney, Sara Mccarthy, Matthew W. Mckown, Joy O'Keefe, Lucy D. Patterson, Kristopher A. Pitcher, Matthew Rustand, Jordi L. Segers, Kyle Seppanen, Jeremy L. Siemers, Christian Stratton, Bethany R. Straw, Theodore J. Weller, Brian E. Reichert

Nebraska Cooperative Fish and Wildlife Research Unit: Staff Publications

1. Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions.

2. Here, we present NABat ML, an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and …

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