A Distributed System for Optimal Scale Feature Extraction and Semantic Classification of Large-Scale Airborne LiDAR Point Clouds

Abstract

Airborne LiDAR (Light Detection and Ranging) or aerial laser scanning (ALS) technology can capture large-scale point cloud data, which represents the topography of large regions. The raw point clouds need to be managed and processed at scale for exploration and contextual understanding of the topographical data. One of the key processing steps is feature extraction from pointwise local geometric descriptors for object-based classification. The state of the art involves finding an optimal scale for computing the descriptors, determined using descriptors across multiple scales, which becomes computationally intensive in the case of big data. Hence, we propose the use of a widely used big data analytics framework integration of Apache Spark and Cassandra, for extracting features at optimal scale, semantic classification using a random forest classifier, and interactive visualization. The visualization involves real-time updates to the selection of regions of interest, and display of feature vectors upon a change in the computation of descriptors. We show the efficacy of our proposed application through our results in the DALES aerial LiDAR point cloud.