Automatic detection of color markings and numbers on trees in point clouds from Personal Laser Scanning (PLS) and Terrestrial Laser Scanning (TLS)

Abstract

In forestry, colored spray paint is used for different purposes, such as marking trees that are chosen for the next harvest or that will be promoted by future silvicultural activities, or for the demarcation of skid/cable roads. Numerous studies have demonstrated the application of Terrestrial Laser Scanners (TLS) and Personal Laser Scanners (PLS) for the acquisition of tree parameters (diameters, heights, and tree positions) in a forest inventory context. However, no studies have included classification based on spectral information by applying colored markings to trees. When a laser scanner is combined with a camera, the color information can be mapped onto the laser point cloud, allowing for additional analysis of spectral information. In this study, we have tested the analysis of joint information from imagery and 3D point clouds in two different settings: (i) PLS point cloud data from a GeoSLAM ZEB Horizon were complemented with spectral information from images collected with a NCTech iSTAR Pulsar 360-degree camera, (ii) TLS point cloud data from a RIEGL VZ-400i were combined with imagery from a Nikon D850 digital single lens reflex (DSLR) camera with 14 mm fisheye lens. The major objective was to automatically classify trees that were marked with colored sprays automatically identify the numbers sprayed on the trees. The marked trees could be classified correctly with a balanced accuracy of 77.26% for PLS and 98.01% for the data collected with the TLS system using a support vector machine (SVM) model. The numbers written on the trees were classified from the TLS data using a YOLOv8 model. The accuracy of the digit classification reached up to 97.30%. In conclusion, it is possible to automatically detect marked trees and to automatically recognize the numbers spayed on these trees from colored point cloud data. However, accuracy strongly depends on the selected combination of scanner and camera. The findings of future research on this subject might differ with the ongoing development of PLS systems and could be enhanced by creating a large dataset of handwritten numbers collected using laser scanning systems.