In this paper, we present a semantic segmentation framework for large-scale 3D point clouds with high spatial resolution. For such data with huge amounts of points, the classification of each individual 3D point is an intractable task. Instead, we propose to segment the scene into meaningful regions as a first step. Afterward, we classify these segments using a combination of PointNet and geometric deep learning. This two-step approach resembles object-based image analysis. As an additional novelty, we apply surface normalization techniques and enrich features with geometric attributes. Our experiments show the potential of this approach for a variety of outdoor scene analysis tasks. In particular, we are able to reach 89.6\% overall accuracy and 64.4\% average intersection over union (IoU) in the Semantic3D benchmark. Furthermore, we achieve 66.7\% average IoU on Paris-Lille-3D. We also successfully apply our approach to the automatic semantic analysis of forestry data.

We propose a deep learning-based framework which can manage large-scale point clouds of outdoor scenes with high spatial resolution. Analogous to Object-Based Image Analysis (OBIA), our approach segments the scene by grouping similar points together to generate meaningful objects. Later, our net classifies segments instead of individual points using an architecture inspired by PointNet, which applies Edge convolutions, making our approach efficient. Usually, Light Detection and Ranging (LiDAR) data do not come together with RGB information. This approach was trained using both RBG and RGB+XYZ information. In some circumstances, LiDAR data presents patterns that do not correspond to the surface object. This mainly occurs when objects partially block beans of light, to address this issue, normalized elevation was included in the analysis to make the model more robust.

Biodiversity changes can be monitored using georeferenced and multitempo-ral data. Those changes refer to the process of automatically identifying differ-ences in the measurements computed over time. The height and the Diameterat Breast Height of the trees can be measured at different times. The mea-surements of individual trees can be tracked over the time resulting in growthrates, tree survival, among other possibles applications. We propose a deeplearning-based framework for semantic segmentation, which can manage largepoint clouds of forest areas with high spatial resolution. Our method divides apoint cloud into geometrically homogeneous segments. Then, a global feature isobtained from each segment, applying a deep learning network called PointNet.Finally, the local information of the adjacent segments is included through anadditional sub-network which applies edge convolutions. We successfully trainand test in a data set which covers an area with multiple trees. Two addi-tional forest areas were also tested. The semantic segmentation accuracy wastested using F1-score for four semantic classes:leaves(F1 = 0.908),terrain(F1 = 0.921),trunk(F1 = 0.848) anddead wood(F1 = 0.835). Furthermore,we show how our framework can be extended to deal with forest measurementssuch as measuring the height of the trees and the DBH.

In this paper, we propose an approach for the semantic segmentation of a 3D point cloud using local 3D moment invariants and the integration of contextual information. Specifically, we focus on the task of analyzing forestal and urban areas which were recorded by terrestrial LiDAR scanners. We demonstrate how 3D moment invariants can be leveraged as local features and that they are on a par with established descriptors. Furthermore, we show how an iterative learning scheme can increase the overall quality by taking neighborhood relationships between classes into account. Our experiments show that the approach achieves very good results for a variety of tasks including both binary and multi-class settings.