  Aerial imagery is increasingly used in Earth science and natural resource
management as a complement to labor-intensive ground-based surveys. Aerial
systems can collect overlapping images that provide multiple views of each
location from different perspectives. However, most prediction approaches (e.g.
for tree species classification) use a single, synthesized top-down
"orthomosaic" image as input that contains little to no information about the
vertical aspects of objects and may include processing artifacts. We propose an
alternate approach that generates predictions directly on the raw images and
accurately maps these predictions into geospatial coordinates using semantic
meshes. This method$\unicode{x2013}$released as a user-friendly open-source
toolkit$\unicode{x2013}$enables analysts to use the highest quality data for
predictions, capture information about the sides of objects, and leverage
multiple viewpoints of each location for added robustness. We demonstrate the
value of this approach on a new benchmark dataset of four forest sites in the
western U.S. that consists of drone images, photogrammetry results, predicted
tree locations, and species classification data derived from manual surveys. We
show that our proposed multiview method improves classification accuracy from
53% to 75% relative to an orthomosaic baseline on a challenging cross-site tree
species classification task.
