Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

ABSTRACT: Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurr

SUBMITTER: Ke TW 

PROVIDER: S-EPMC10990224 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature