  In situ imageomics leverages machine learning techniques to infer biological
traits from images collected in the field, or in situ, to study individuals
organisms, groups of wildlife, and whole ecosystems. Such datasets provide
real-time social and environmental context to inferred biological traits, which
can enable new, data-driven conservation and ecosystem management. The
development of machine learning techniques to extract biological traits from
images are impeded by the volume and quality data required to train these
models. Autonomous, unmanned aerial vehicles (UAVs), are well suited to collect
in situ imageomics data as they can traverse remote terrain quickly to collect
large volumes of data with greater consistency and reliability compared to
manually piloted UAV missions. However, little guidance exists on optimizing
autonomous UAV missions for the purposes of remote sensing for conservation and
biodiversity monitoring. The UAV video dataset curated by KABR: In-Situ Dataset
for Kenyan Animal Behavior Recognition from Drone Videos required three weeks
to collect, a time-consuming and expensive endeavor. Our analysis of KABR
revealed that a third of the videos gathered were unusable for the purposes of
inferring wildlife behavior. We analyzed the flight telemetry data from
portions of UAV videos that were usable for inferring wildlife behavior, and
demonstrate how these insights can be integrated into an autonomous remote
sensing system to track wildlife in real time. Our autonomous remote sensing
system optimizes the UAV's actions to increase the yield of usable data, and
matches the flight path of an expert pilot with an 87% accuracy rate,
representing an 18.2% improvement in accuracy over previously proposed methods.
