Automated Classification of Airborne Laser Scanning Point Clouds
This addresses the need for efficient processing of large point cloud data in applications like flooding protection and forestry, though it appears incremental in method.
The paper tackles the problem of automatically classifying ground cover and soil types from airborne laser scanning point clouds, finding that their approach using decision trees with beam vector components and a genetic algorithm delivers consistently high-quality classifications that surpass classical methods.
Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods.