The Iqmulus urban showcase: Automatic tree classification and identification in huge mobile mapping point clouds

Böhm, J.; Bredif, M.; Gierlinger, T.; Krämer, M.; Lindenbergh, R.C.; Liu, K.; Michel, F.; Sirmacek, B.

doi:10.5194/isprsarchives-XLI-B3-301-2016

The Iqmulus urban showcase

Title

The Iqmulus urban showcase: Automatic tree classification and identification in huge mobile mapping point clouds

Author

Böhm, J. (University College London (UCL))
Bredif, M. (Université Paris-Est)
Gierlinger, T. (Fraunhofer Institute for Computer Graphics Research IGD)
Krämer, M. (Fraunhofer Institute for Computer Graphics Research IGD)
Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing)
Liu, K. (TU Delft Geo-engineering; University College London (UCL))
Michel, F. (Fraunhofer Institute for Computer Graphics Research IGD)
Sirmacek, B. (TU Delft Optical and Laser Remote Sensing)

Date

2016

Abstract

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.

Subject

Big data
Classification
Cloud computing
Mobile mapping
Trees
Web-based visualization

To reference this document use:

http://resolver.tudelft.nl/uuid:bdfe31e4-d9bd-4cdd-8b0e-2a74d9c3ad01

DOI

https://doi.org/10.5194/isprsarchives-XLI-B3-301-2016

ISSN

1682-1750

Source

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 301-307

Part of collection

Institutional Repository