  Data collection for forestry, timber, and agriculture currently relies on
manual techniques which are labor-intensive and time-consuming. We seek to
demonstrate that robotics offers improvements over these techniques and
accelerate agricultural research, beginning with semantic segmentation and
diameter estimation of trees in forests and orchards. We present TreeScope
v1.0, the first robotics dataset for precision agriculture and forestry
addressing the counting and mapping of trees in forestry and orchards.
TreeScope provides LiDAR data from agricultural environments collected with
robotics platforms, such as UAV and mobile robot platforms carried by vehicles
and human operators. In the first release of this dataset, we provide
ground-truth data with over 1,800 manually annotated semantic labels for tree
stems and field-measured tree diameters. We share benchmark scripts for these
tasks that researchers may use to evaluate the accuracy of their algorithms.
Finally, we run our open-source diameter estimation and off-the-shelf semantic
segmentation algorithms and share our baseline results.
