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The IQmulus Urban Showcase: Automatic Tree Classification and Identification in Huge Mobile Mapping Point Clouds

: Böhm, J.; Bredif, Mathieu; Gierlinger, Thomas; Krämer, Michel; Lindenbergh, R.; Liu, K.; Michel, Frank; Sirmacek, B.

Postprint urn:nbn:de:0011-n-4212941 (1.5 MByte PDF)
MD5 Fingerprint: 5f217d90cb8d9b1159ff5de218a9d41d
Created on: 20.9.2017

Halounova, L. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
XXIII ISPRS Congress 2016. Commission III : 12-19 July 2016, Prague, Czech Republic; From Human History to the Future with Spatial Information
Istanbul: ISPRS, 2016 (ISPRS Archives XLI-B3)
International Society for Photogrammetry and Remote Sensing (ISPRS Congress) <23, 2016, Prague>
Conference Paper, Electronic Publication
Fraunhofer IGD ()
Big Data; classifications; Cloud Computing; web-based visualization; mobile mapping; Guiding Theme: Smart City; Research Area: Computer vision (CV); Research Area: Modeling (MOD)

Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.