Data collection for forestry, timber, and agriculture currently relies on manual techniques which are labor-intensive and time-consuming. TreeScope is the first semantically segmented lidar dataset collected with robotic systems in agricultural environments. It provides lidar data and ground-truth data for semantic segmentation and diameter estimation from agricultural environments to address the counting and mapping of trees in forestry and orchards. This example visualizes data from their custom sensor platform, including sensor data from lidar, IMU, GPS, RGBD and thermal cameras, along with ground-truth data with over 1,800 manually annotated semantic labels for the tree stems and field measurements of tree diameters. Check out their Github repo for an overview on how to use the data and benchmark scripts to evaluate the performance of diameter estimation and semantic segmentation algorithms, along with their website for more info. buff.ly/4cqZ94o The Treescope dataset has been provided by the research of Vijay Kumar, Dean of Penn Engineering and Kumar Robotics, Derek Cheng, Fernando Cladera, Ankit Prabhu, Xu Liu, Alan Zhu, Patrick Corey Green, Reza Ehsani, and Pratik Chaudhari.