Automatic classification of common building materials from 3D terrestrial laser scan data

Abstract

Automatic building material classification has been a popular research interest over the past decades because it is useful for construction management and facility management. Currently, the proposed methods for automatic material classification are mainly based on 2D images by using the visual features of building materials. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contain the surface geometries of building materials. The laser scan data include not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more attributes provided, laser scan data have the potential to improve the accuracy of building material classification. Therefore, this research aims to develop a TLS data-based classification method for common building materials using machine learning techniques. The developed technique uses material reflectance, HSV colour values, and surface roughness as the features for material classification. A database containing the laser scan data of ten common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average classification accuracy of 96.7%, which demonstrated the feasibility of the developed method.