AUTOMATIC CLASSIFICATION OF BRIDGES AND CONTINENTAL WATER BODIES FROM 3D POINT CLOUDS (AERIAL LIDAR)

Abstract. The use of algorithms for automatic classification of aerial laser scanner 3D Point Clouds is the main process that improves its thematic quality. The main objectives of using 3D Point Clouds are the description of the surface and the detection of objects. The aim of this proposal for bridge and water detection algorithms is to increase the range and accuracy of the classification parameters of these products obtained with LiDAR technologies. With this methodology, the Digital Elevation Models (DEM) quality is improved and they are obtained by automated models of bridges and hydrography.

This paper describes a methodology to detect and classify bridges and continental water bodies in points using the properties of LiDAR technology such as radiometric and geometric variables implementing indexes like NDVI, NDWI or NFC. In addition, the Network of Roads and Hydrographic models in Spain are used to reduce the area of interest and errors. Part of the province of Teruel (Spain) has been used as study area.