USING MACHINE LEARNING TO MANAGE LARGE REMOTE CAMERA DATASETS AND DETECT SAN JOAQUIN FOX IN WESTERN MERCED COUNTY
|Ryan B Avery; Development Seed; firstname.lastname@example.org; Steven Avery
As a requirement of the Habitat Conservation Plan prepared for the Wright Solar Park project, ICF has used 10 remote cameras annually since 2020 to determine if San Joaquin kit fox (Vulpes macrotis mutica) are present. Unbaited camera stations were established along the fence line of the solar facility and continuously collected images for 4 months in 2020 (May-August) and for 7 months in 2021 and 2022 (February-August). Tens of thousands of images were collected each year. Traditionally, these large image collections are reviewed by humans, who need to sift through many uninteresting images. To improve this process, we created a data processing pipeline using Microsoft’s open-source Megadetector and Species Classification machine learning models, developed from millions of examples of camera trap images. At the project site, we were able to filter out most images without objects of interest, leaving a manageable number of images for human review. The results of the surveys have confirmed the presence of San Joaquin kit fox at the site each year. There were 5 detections in 2020, 9 detections in 2021, and 19 detections in 2022. We present methods for calibrating and running these models on large image collections typical of long-term monitoring projects.