A method for the automatic detection of roads from airborne laser scanner data is presented. Traditionally, intensity information has not been used in feature extraction from LIDAR data because the data is too noisy. This article deals with using as much of the recorded laser information as possible thus both height and intensity are used. To extract roads from a LIDAR point cloud, a hierarchical classification technique is used to classify the LIDAR points progressively into road or non-road. Initially, an accurate digital terrain model (DTM) model is created by using successive morphological openings with different structural element sizes. Individual laser points are checked for both a valid intensity range and height difference from the subsequent DTM. A series of filters are then passed over the road candidate image to improve the accuracy of the classification. The success rate of road detection and the level of detail of the resulting road image both depend on the resolution of the laser scanner data and the types of roads expected to be found. The presence of road-like features within the survey area such as private roads and car parks is discussed and methods to remove this information are entertained. All algorithms used are described and applied to an example urban test site
