An open dataset for individual tree detection in UAV LiDAR point clouds and RGB orthophotos in dense mixed forests
Ivan Dubrovin, Clément Fortin, Alexander Kedrov
Abstract
We present an open access dataset for development, evaluation, and comparison of algorithms for individual tree detection in dense mixed forests. The dataset consists of a detailed field inventory and overlapping UAV LiDAR and RGB orthophoto, which make it possible to develop algorithms that fuse multimodal data to improve detection results. Along with the dataset, we describe and implement a basic local maxima filtering baseline and an algorithm for automatically matching detection results to the ground truth trees for detection algorithm evaluation.
Topics & Concepts
OrthophotoComputer sciencePoint cloudLidarRGB color modelTree (set theory)Ground truthArtificial intelligenceMatching (statistics)Fuse (electrical)Field (mathematics)Remote sensingComputer visionPattern recognition (psychology)GeographyMathematicsStatisticsMathematical analysisElectrical engineeringPure mathematicsEngineeringRemote Sensing and LiDAR Applications3D Surveying and Cultural HeritageRemote Sensing in Agriculture