Heuristic generation of multispectral labeled point cloud datasets for deep learning models

Abstract. Deep Learning (DL) models need big enough datasets for training, especially those that deal with point clouds. Artificial generation of these datasets can complement the real ones by improving the learning rate of DL architectures. Also, Light Detection and Ranging (LiDAR) scanners can be studied by comparing its performing with artificial point clouds. A methodology for simulate LiDAR-based artificial point clouds is presented in this work in order to get train datasets already labelled for DL models. In addition to the geometry design, a spectral simulation will be also performed so that all points in each cloud will have its 3 dimensional coordinates (x, y, z), a label designing which category it belongs to (vegetation, traffic sign, road pavement, …) and an intensity estimator based on physical properties as reflectance.Ministerio de Ciencia, Innovación y Universidades | Ref. PCI2020-120705-

Individual tree segmentation in deciduous forests using geodesic voting

Airborne Laser Scanning (ALS) has been widely used to survey forest areas. The extraction (segmentation) of individual trees from ALS point clouds is a prerequisite step for tree biophysical parameter estimation. For this purpose, we develop and evaluate a graph based segmentation algorithm adapted to deciduous forests scanned with high density Li-DAR (∼50 points / m2) in leaf-off conditions. The algorithm is applied to a 1 ha deciduous forest plot in western Switzerland and the accuracy of individual trunk locations is evaluated in terms of recall, precision and F-score. The results indicate that the algorithm performs satisfactorily within the experimental setup conditions.</p