Application of Machine Learning to Detect Building Points in Photogrammetry-based Point Clouds

Abstract

Different point cloud technologies such as Terrestrial Laser Scanners (TLS), Airborne Laser Scanners (ALS), Mobile Mapping Systems (MMS), and Unmanned Aerial Vehicles (UAV) have become increasingly more common in land surveying and geoinformatics over recent years. Thanks to these modern tools, experts can survey large areas cost-effectively with either high resolution or high accuracy. However, processing the point cloud, which consists of millions of points, can be a massive challenge. Manual processing of these large datasets can often be very time-consuming and hardware-demanding, and most of the time, only a limited part of the point cloud is used to derive the final products. The solution can be to automate the process as much as possible. Several advanced mathematical methods, especially Machine Learning (ML) algorithms, allow efficient automated processing of point clouds. This paper presents a processing chain to detect and separate building points from large-scale photogrammetry-based point clouds. The processing is based on the combination of Random Sample Consensus (RANSAC) and Machine Learning (ML) algorithms like Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Multi-Layer Perceptron (MLP). Presented methods were trained and tested on established and open available Heissigheim 3D (H3D) dataset to separate roof and vegetation points with over 90% accuracy in order to enhance the separation of building points on large-scale point clouds.