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

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 41, Issue , 2016, Pages 301-307

  Bohm J ^a   Bredif, M ^b   Gierlinger, T ^c   Kramer M ^c   Lindenbergh, R ^d   Liu, K ^a   Michel, F ^c   Sirmacek, B ^d  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITÉ PARIS EST   (France)

^c FRAUNHOFER IGD   (Germany)

^d DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

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

Indexed keywords

AUTOMATION; CLASSIFICATION (OF INFORMATION); CLOUD COMPUTING; CLUSTER COMPUTING; DATA VISUALIZATION; FILE ORGANIZATION; FORESTRY; MAPPING; REMOTE SENSING; TREES (MATHEMATICS); VISUALIZATION;

AUTOMATIC IDENTIFICATION; CLASSIFICATION AND IDENTIFICATIONS; DIMENSIONALITY ANALYSIS; HADOOP DISTRIBUTED FILE SYSTEMS; LASER SCANNING SYSTEMS; MOBILE MAPPING; TREES; WEB-BASED VISUALIZATION;

BIG DATA;

EID: 84978159544     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5194/isprsarchives-XLI-B3-301-2016     Document Type: Conference Paper

References (9)

1. Behr, J., Mouton, C., Parfouru, S., Champeau, J., Jeulin, C., Thöner, M., Stein, C., Schmitt, M., Limper, M., de Sousa, M. and Franke, T.A. (2015). webVis/instant3DHub: visual computing as a service infrastructure to deliver adaptive, secure and scalable user centric data visualisation. In Proceedings of the 20th International Conference on 3D Web Technology, ACM, pp. 39-47.
2. Demantke, J., Mallet, C., David, N. and Vallet, B., (2011). Dimensionality based scale selection in 3D lidar point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(Part 5), p.W12.
3. Krämer, M., (2014). Controlling the processing of smart city data in the cloud with domain-specific languages. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (pp. 824-829).
4. Krämer, M. and Senner, I., (2015). A modular software architecture for processing of big geospatial data in the cloud. Computers & Graphics, 49, pp.69-81.
5. Lindenbergh, R.C., Berthold, D., Sirmacek, B., Herrero-Huerta, M., Wang, J. and Ebersbach, D., (2015). Automated large scale parameter extraction of road-side trees sampled by a laser mobile mapping system. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(3), p.589.
6. Liu, K. and Böhm, J., (2015). Classification of Big Point Cloud Data Using Cloud Computing. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(3), p.553.
7. Paparoditis, N., Papelard, J.P., Cannelle, B., Devaux, A., Soheilian, B., David, N. and Houzay, E., (2012). Stereopolis II: A multi-purpose and multi-sensor 3D mobile mapping system for street visualisation and 3D metrology. Revue française de photogramm etrie et de teĺedetection, 200(1), pp.69-79.
8. Schütz (2014), Potree http://potree.org, accessed 2016-04-13
9. Apache Spark, (2016). A fast and general engine for large-scale data processing. http://spark.apache.org/, accessed 2016-04-15