Semantic segmentation of LiDAR points clouds: Rasterisation beyond Digital Elevation Models

International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they provide rich information to be used independently or together with optical data sources such as aerial imagery. However, their non-structured and sparse nature make them difficult to handle, conversely to raw imagery for which many efficient tools are available. To overcome this specific nature of LiDAR point clouds, standard approaches often rely in converting the point cloud into a digital elevation model, represented as a 2D raster. Such a raster can then be used similarly as optical images, e.g. with 2D convolutional neural networks for semantic segmentation. In this letter, we show that LiDAR point clouds provide more information than only the DEM, and that considering alternative rasterization strategies helps to achieve better semantic segmentation results. We illustrate our findings on the IEEE DFC 2018 dataset