Towards Urban Tree Recognition in Airborne Point Clouds with Deep 3D Single-Shot Detectors.

Automatic mapping of individual urban trees is increasingly important to city administration and planing. Although deep learning algorithms are now standard methodology in computer vision, their adaption to individual tree detection in urban areas has hardly been investigated so far. In this work, we propose a deep single-shot object detection network to find urban trees in point clouds from airborne laser scanning. The network consists of a sparse 3D convolutional backbone for feature extraction and a subsequent single-shot region proposal network for the actual detection. It takes as input raw 3D voxel clouds, discretized from the point cloud in preprocessing. Outputs are cylindrical tree objects paired with their detection scores. We train and evaluate the network on the ISPRS Vaihingen 3D Benchmark dataset with custom tree object labels. The general feasibility of our approach is demonstrated. It achieves promising results compared to a traditional 2D baseline using watershed segmentation. We also conduct comparisons with state-of-the-art machine learning methods for semantic point segmentation.