TreeNet: An AI-Powered Software for Individual Tree Detection and Forest Inventory Using UAV LiDAR Data

TreeNet is an AI-powered software solution designed to automatically detect, segment, and analyze individual trees using UAV-collected (drone) LiDAR and photogrammetric point clouds. Developed by Sina Jarahizadeh and Bahram Salehi, TreeNet provides intelligent measurements of tree height, crown area, structural attributes, biomass, and species across both urban and forested landscapes.

TreeNet leverages a novel deep learning architecture inspired by single-stage object detectors. This enables high accuracy, adaptability to diverse environments, and user-friendly operation. Unlike conventional methods which rely on manual fieldwork or generic 2D canopy tools. TreeNet offers a fully automated approach that significantly reduces the time, labor, and cost associated with tree-level analysis at scale.

What sets TreeNet apart is its ability to perform individual tree segmentation and attribute extraction on a large scale-something not achievable with existing tools. Traditionally, this work has required human operators to measure trees manually in the field. TreeNet eliminates that need by automating the entire process through advanced AI.

To date, we have developed and implemented TreeNet as a functional software toolkit and validated its performance across a variety of real-world environments.

Designed for seamless integration with drone platforms, TreeNet empowers government agencies, environmental organizations, industry professionals, and researchers to make better-informed decisions through high-resolution, AI-driven forest intelligence.

TreeNet serves a diverse range of customers across government, environmental, and private sectors who require accurate, scalable, and cost-effective tree-level data at large scale. Some of the potential customers include:

• Drone service providers
• Timber and forestry companies
• Urban planners, landscape architects, and green space managers
• Environmental consulting and carbon offset firms
• Municipalities and urban forestry departments
• Government agencies (e.g., US Forest Service)

ARSenAL: Advanced Remote Sensing Applied Lab

ARSenAL is an advanced remote sensing toolbox, developed by Dr. Salehi and his team, comprising several algorithms for processing SAR and optical imagery. Algorithms include Segmentation, Feature Extraction (Spectral and Textural), Object-based Random Forest Classification, SAR speckle filtering and decomposition, a genetic-based feature extraction for random forest classification of wetlands, etc.