Authors:Johannes Wolf;Rico Richter andJürgen Döllner

Affiliation:Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Germany

Keyword(s):LiDAR, 3D Point Clouds, Digital Image Analysis, Semantic Classification.

Abstract:This work presents an approach for the automated detection of railroad assets in 3D point clouds from mobile mapping LiDAR scans using established convolutional neural networks for image analysis. It describes how images of individual scan lines can be generated from 3D point clouds. In these scan lines, objects such as tracks, signal posts, and axle counters can be detected using artificial neural networks for image analysis, previously trained on ground-truth data. The recognition results can then be transferred back to the 3D point cloud as a semantic classification result, or they are used to generate geometry or map data for further processing in GIS applications. Using this approach, trained objects can be found with high automation. Challenges such as varying point density, different data characteristics of scanning devices, and the massive amount of data can be overcome with this approach.