  Accurate and consistent methods for counting trees based on remote sensing
data are needed to support sustainable forest management, assess climate change
mitigation strategies, and build trust in tree carbon credits. Two-dimensional
remote sensing imagery primarily shows overstory canopy, and it does not
facilitate easy differentiation of individual trees in areas with a dense
canopy and does not allow for easy separation of trees when the canopy is
dense. We leverage the fusion of three-dimensional LiDAR measurements and 2D
imagery to facilitate the accurate counting of trees. We compare a deep
learning approach to counting trees in forests using 3D airborne LiDAR data and
2D imagery. The approach is compared with state-of-the-art algorithms, like
operating on 3D point cloud and 2D imagery. We empirically evaluate the
different methods on the NeonTreeCount data set, which we use to define a
tree-counting benchmark. The experiments show that FuseCountNet yields more
accurate tree counts.
