Tree-Net: An AI-Driven Software for 3D Forest Modeling, Including Automated Tree Detection and Measurement (Height, Crown Area, Volume, Biomass, and Species) Using Drone LiDAR in Urban and Forest…

AiDroneTree, an AI-based software powered by TreeNet, automatically detects, maps and measures individual trees from 3D point clouds derived from LiDAR or photogrammetry, producing tree-level data on location, height, crown dimensions, volume, biomass and species in GIS-compatible formats. The technology, which its developers say has been validated through real-world deployments and peer-reviewed research, targets urban tree inventory and utility vegetation management, with additional uses in forest inventory and biomass and carbon assessment. A PCT international patent application was filed August 25, 2026, the software is rated at Technology Readiness Level 6, and it is available for licensing.

AiDroneTree™ powered by TreeNet is an AI-based software technology that automatically detects, maps, and measures individual trees from three-dimensional 3D point cloud data derived from LiDAR or photogrammetry. It supports scalable urban tree inventories, utility vegetation management, and forest monitoring through GIS-compatible tree-level outputs. Accurate, current information about individual trees is important for urban forestry, utility vegetation management, forest inventory, and environmental monitoring. Conventional field-based tree inventories can be time-consuming and expensive to update across large or difficult-to-access areas. LiDAR and photogrammetry can produce detailed 3D point clouds, but converting these complex datasets into reliable, individual-tree records remains challenging, particularly where crowns overlap or vegetation is dense. Automated approaches that detect individual trees and deliver practical, GIS-compatible outputs can help organizations use existing and newly acquired 3D data more efficiently. AiDroneTree™ is an AI-powered software technology for individual-tree detection, delineation, and structural characterization using 3D point cloud data derived from LiDAR or photogrammetry. It can process previously acquired datasets as well as newly collected data, including UAV-based observations. The software prepares point cloud data, generates informative raster representations, and uses machine learning and semantic segmentation to detect individual trees by clustering the corresponding 3D points. It then produces tree-level information such as location, height, crown dimensions, and other structural metrics, with results exportable in GIS-compatible formats. Its primary commercial applications are urban tree inventory and utility vegetation management; additional applications include forest inventory and biomass-related assessments. Validated through real-world deployments and peer-reviewed research, the technology offers a scalable, user-friendly solution that overcomes the limitations of traditional urban forest inventory and utility vegetation management, among other applications Photo for reference only, not a depiction of the invention. • Automates individual-tree detection and measurement, reducing reliance on labor-intensive manual inventory workflows. • Processes 3D point clouds derived from LiDAR and photogrammetry, including previously acquired datasets. • Uses AI-based tree detection and 3D point association to derive tree locations, heights, crown dimensions, and other structural metrics. • Generates GIS-compatible tree-level inventory outputs for integration into existing geospatial workflows. • Supports urban tree inventory, utility vegetation management, forest inventory, and biomass-related assessments. • Offers a software-based workflow that can be applied to tree-containing environments, including streetscapes, utility corridors, and forests. • Supports repeatable inventory updates and evaluation across datasets and environments. • Urban tree inventory and management • Utility vegetation management • Forest inventory and monitoring • Biomass and carbon assessment where supported by suitable models and validation • Environmental and ecological research Patent pending — PCT international application filed August 25, 2026. TRL 6 This technology is available for licensing.