  LiDAR point clouds contain measurements of complicated natural scenes and can
be used to update digital elevation models, glacial monitoring, detecting
faults and measuring uplift detecting, forest inventory, detect shoreline and
beach volume changes, landslide risk analysis, habitat mapping, and urban
development, among others. A very important application is the classification
of the 3D cloud into elementary classes. For example, it can be used to
differentiate between vegetation, man-made structures, and water. Our goal is
to present a preliminary comparison study for the classification of 3D point
cloud LiDAR data that includes several types of feature engineering. In
particular, we demonstrate that providing context by augmenting each point in
the LiDAR point cloud with information about its neighboring points can improve
the performance of downstream learning algorithms. We also experiment with
several dimension reduction strategies, ranging from Principal Component
Analysis (PCA) to neural network-based auto-encoders, and demonstrate how they
affect classification performance in LiDAR point clouds. For instance, we
observe that combining feature engineering with a dimension reduction a method
such as PCA, there is an improvement in the accuracy of the classification with
respect to doing a straightforward classification with the raw data.
