Building Extraction From LiDAR Data Applying Deep Convolutional Neural Networks

Deep learning paradigm has been shown to be a very efficient classification framework for many application scenarios, including the analysis of Light Detection and Ranging (LiDAR) data for building detection. In fact, deep learning acts as a set of mathematical transformations, encoding the raw input data into appropriate forms of representations that maximize the classification performance. However, it is clear that mathematical computations alone, even highly nonlinear, are not adequate to model the physical properties of a problem, distinguishing, for example, the building structures from vegetation. In this letter, we address this difficulty by augmenting the raw LiDAR data with features coming from a physical interpretation of the information. Then, we exploit a deep learning paradigm based on a convolutional neural network model to find out the best input representations suitable for the classification. As test sites, three complex urban study areas with various kinds of building structures through the LiDAR data set of Vaihingen, Germany were selected. Our method has been evaluated in the context of “ISPRS Test Project on Urban Classification and 3-D Building Reconstruction.” Comparisons with traditional methods, such as artificial neural networks and support vector machine-based classifiers, indicate the outperformance of the proposed approach in terms of robustness and efficiency.