D. Munoz, J. A. Bagnell, N. Vandapel, and M. Hebert, Contextual classification with functional Max-Margin Markov Networks, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.20-25, 2009.
DOI : 10.1109/CVPR.2009.5206590

X. Xiong, D. Munoz, J. A. Bagnell, and M. Hebert, 3-D scene analysis via sequenced predictions over points and regions, 2011 IEEE International Conference on Robotics and Automation, pp.9-13, 2011.
DOI : 10.1109/ICRA.2011.5980125
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.187.6912

H. Hu, D. Munoz, J. A. Bagnell, and M. Hebert, Efficient 3-D scene analysis from streaming data, Proceedings of the IEEE International Conference on Robotics and Automation, pp.6-10, 2013.

M. Brédif, B. Vallet, A. Serna, B. Marcotegui, N. Paparoditis et al., IQmulus urban point cloud classification benchmark, Proceedings of the IQmulus Workshop on Processing Large Geospatial Data, pp.1-6, 2014.

M. Weinmann, S. Urban, S. Hinz, and B. Jutzi, Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas, Computers & Graphics, vol.49, pp.47-57, 2015.
DOI : 10.1016/j.cag.2015.01.006

M. Weinmann, B. Jutzi, S. Hinz, and C. Mallet, Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers, ISPRS Journal of Photogrammetry and Remote Sensing, vol.105, pp.286-304, 2015.
DOI : 10.1016/j.isprsjprs.2015.01.016

T. Hackel, J. D. Wegner, and K. Schindler, Fast semantic segmentation of 3D point clouds with strongly varying density, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, vol.2016, pp.3-177

C. A. Vanegas, D. G. Aliaga, and B. Benes, Automatic Extraction of Manhattan-World Building Masses from 3D Laser Range Scans, IEEE Transactions on Visualization and Computer Graphics, vol.18, issue.10, pp.1627-1637, 2012.
DOI : 10.1109/TVCG.2012.30

A. Boyko and T. Funkhouser, Extracting roads from dense point clouds in large scale urban environment, ISPRS Journal of Photogrammetry and Remote Sensing, vol.66, issue.6, pp.2-12, 2011.
DOI : 10.1016/j.isprsjprs.2011.09.009
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.2422

L. Zhou and G. Vosselman, Mapping curbstones in airborne and mobile laser scanning data, International Journal of Applied Earth Observation and Geoinformation, vol.18, pp.293-304, 2012.
DOI : 10.1016/j.jag.2012.01.024

H. Guan, J. Li, Y. Yu, C. Wang, M. Chapman et al., Using mobile laser scanning data for automated extraction of road markings, ISPRS Journal of Photogrammetry and Remote Sensing, vol.87, pp.93-107, 2014.
DOI : 10.1016/j.isprsjprs.2013.11.005

S. Pu, M. Rutzinger, and G. Vosselman, Recognizing basic structures from mobile laser scanning data for road inventory studies, ISPRS Journal of Photogrammetry and Remote Sensing, vol.66, issue.6, pp.28-39, 2011.
DOI : 10.1016/j.isprsjprs.2011.08.006

B. Gorte, S. Oude-elberink, B. Sirmacek, and J. Wang, IQPC 2015 TRACK: TREE SEPARATION AND CLASSIFICATION IN MOBILE MAPPING LIDAR DATA, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, issue.3, pp.607-612
DOI : 10.5194/isprsarchives-XL-3-W3-607-2015
URL : http://doi.org/10.5194/isprsarchives-xl-3-w3-607-2015

B. Sirmacek and R. Lindenbergh, AUTOMATIC CLASSIFICATION OF TREES FROM LASER SCANNING POINT CLOUDS, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, issue.5, pp.3-137
DOI : 10.5194/isprsannals-II-3-W5-137-2015

R. C. Lindenbergh, D. Berthold, B. Sirmacek, M. Herrero-huerta, J. Wang et al., AUTOMATED LARGE SCALE PARAMETER EXTRACTION OF ROAD-SIDE TREES SAMPLED BY A LASER MOBILE MAPPING SYSTEM, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, issue.3, pp.589-594
DOI : 10.5194/isprsarchives-XL-3-W3-589-2015

M. Kelly, Urban trees and the green infrastructure agenda, Proceedings of the Urban Trees Research Conference, pp.13-14, 2011.

J. L. Edmondson, I. Stott, Z. G. Davies, K. J. Gaston, and J. Leake, Soil surface temperatures reveal moderation of the urban heat island effect by trees and shrubs. Sci. Rep, pp.1-8, 2016.

J. D. Wegner, S. Branson, D. Hall, K. Schindler, and P. Perona, Cataloging Public Objects Using Aerial and Street-Level Images ??? Urban Trees, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.6014-6023, 2016.
DOI : 10.1109/CVPR.2016.647

Z. Zhang, S. Fidler, and R. Urtasun, Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.669-677, 2016.
DOI : 10.1109/CVPR.2016.79
URL : http://arxiv.org/abs/1512.06735

N. Silberman, D. Sontag, and R. Fergus, Instance Segmentation of Indoor Scenes Using a Coverage Loss, Proceedings of the European Conference on Computer Vision, pp.6-12, 2014.
DOI : 10.1007/978-3-319-10590-1_40

M. Weinmann, C. Mallet, and M. Brédif, Detection, segmentation and localization of individual trees from mms point cloud data, GEOBIA 2016 : Solutions and Synergies, pp.14-16, 2016.
DOI : 10.3990/2.388

M. Weinmann, Reconstruction and Analysis of 3D Scenes?From Irregularly Distributed 3D Points to Object Classes, 2016.

T. Melzer, Non-parametric segmentation of ALS point clouds using mean shift, Journal of Applied Geodesy, vol.71, issue.3, pp.159-170, 2007.
DOI : 10.1016/j.isprsjprs.2005.12.001

G. Vosselman, Point cloud segmentation for urban scene classification, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.7, issue.2, pp.257-262
DOI : 10.5194/isprsarchives-XL-7-W2-257-2013

I. Lee and T. Schenk, Perceptual organization of 3D surface points, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, pp.3-193, 2002.

L. Linsen and H. Prautzsch, Local versus global triangulations, Proceedings of Eurographics, pp.257-263, 2001.

S. Filin and N. Pfeifer, Neighborhood Systems for Airborne Laser Data, Photogrammetric Engineering & Remote Sensing, vol.71, issue.6, pp.743-755, 2005.
DOI : 10.14358/PERS.71.6.743

M. Pauly, R. Keiser, and M. Gross, Multi-scale Feature Extraction on Point-Sampled Surfaces, Computer Graphics Forum, vol.30, issue.2, pp.81-89, 2003.
DOI : 10.1007/978-1-4757-1904-8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.2293

N. J. Mitra and A. Nguyen, Estimating surface normals in noisy point cloud data, Proceedings of the Annual Symposium on Computational Geometry, pp.8-10, 2003.
DOI : 10.1145/777837.777840

J. F. Lalonde, R. Unnikrishnan, N. Vandapel, and M. Hebert, Scale selection for classification of point-sampled 3D surfaces, Proceedings of the International Conference on 3-D Digital Imaging and Modeling, pp.13-16, 2005.

J. Demantké, C. Mallet, N. David, and B. Vallet, DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.5, issue.12, pp.5-97, 2011.
DOI : 10.5194/isprsarchives-XXXVIII-5-W12-97-2011

N. Brodu and D. Lague, 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology, ISPRS Journal of Photogrammetry and Remote Sensing, vol.68, pp.121-134, 2012.
DOI : 10.1016/j.isprsjprs.2012.01.006
URL : https://hal.archives-ouvertes.fr/insu-00700970

J. Niemeyer, F. Rottensteiner, and U. Soergel, Contextual classification of lidar data and building object detection in urban areas, ISPRS Journal of Photogrammetry and Remote Sensing, vol.87, pp.152-165, 2014.
DOI : 10.1016/j.isprsjprs.2013.11.001

R. Blomley, B. Jutzi, and M. Weinmann, Classification of airborne laser scanning data using geometric multi-scale features and different neighbourhood types, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, vol.2016, pp.3-169

C. M. Gevaert, C. Persello, G. K. Vosselman, B. N. Webb, J. R. Lersch et al., Optimizing multiple kernel learning for the classification of UAV data. Remote Sens Context-driven automated target detection in 3-D data, Proc. SPIE, pp.133-143, 2004.

C. Mallet, F. Bretar, M. Roux, U. Soergel, and C. Heipke, Relevance assessment of full-waveform lidar data for urban area classification, ISPRS Journal of Photogrammetry and Remote Sensing, vol.66, issue.6, pp.71-84, 2011.
DOI : 10.1016/j.isprsjprs.2011.09.008

B. Guo, X. Huang, F. Zhang, and G. Sohn, Classification of airborne laser scanning data using JointBoost, ISPRS Journal of Photogrammetry and Remote Sensing, vol.100, pp.71-83, 2015.
DOI : 10.1016/j.isprsjprs.2014.04.015

G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, vol.14, issue.1, pp.55-63, 1968.
DOI : 10.1109/TIT.1968.1054102

M. Weinmann, B. Jutzi, and C. Mallet, Feature relevance assessment for the semantic interpretation of 3D point cloud data, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.5, issue.2, pp.313-318
DOI : 10.5194/isprsannals-II-5-W2-313-2013

K. Khoshelham and S. J. Oude-elberink, Role of dimensionality reduction in segment-based classification of damaged building roofs in airborne laser scanning data, Proceedings of the International Conference on Geographic Object Based Image Analysis, pp.7-9, 2012.

K. Schindler, An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.11, pp.4534-4545, 2012.
DOI : 10.1109/TGRS.2012.2192741

R. Shapovalov, A. Velizhev, and O. Barinova, Non-associative Markov networks for 3D point cloud classification, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, vol.2010, pp.3-103

J. Niemeyer, F. Rottensteiner, and U. Soergel, CONDITIONAL RANDOM FIELDS FOR LIDAR POINT CLOUD CLASSIFICATION IN COMPLEX URBAN AREAS, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, pp.3-263
DOI : 10.5194/isprsannals-I-3-263-2012

A. Schmidt, J. Niemeyer, F. Rottensteiner, and U. Soergel, Contextual Classification of Full Waveform Lidar Data in the Wadden Sea, IEEE Geoscience and Remote Sensing Letters, vol.11, issue.9, pp.1614-1618, 2014.
DOI : 10.1109/LGRS.2014.2302317

M. Weinmann, A. Schmidt, C. Mallet, S. Hinz, F. Rottensteiner et al., Contextual classification of point cloud data by exploiting individual 3D neighborhoods, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, vol.2015, issue.4, pp.3-271

R. Shapovalov, D. Vetrov, and P. Kohli, Spatial Inference Machines, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.23-28, 2013.
DOI : 10.1109/CVPR.2013.384
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.3746

D. Maturana and S. Scherer, VoxNet: A 3D Convolutional Neural Network for real-time object recognition, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.922-928, 2015.
DOI : 10.1109/IROS.2015.7353481

Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang et al., A deep representation for volumetric shapes, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.7-12, 2015.

G. Riegler, A. O. Ulusoy, A. Geiger, and . Octnet, Learning deep 3D representations at high resolutions. arXiv 2017, arXiv:1611.05009v3. 51. Savinov, N. Point Cloud Semantic Segmentation via Deep 3D Convolutional Neural Network Available online: https://github, p.3, 2017.

J. Huang and S. You, Point cloud labeling using 3D Convolutional Neural Network, 2016 23rd International Conference on Pattern Recognition (ICPR), pp.4-8, 2016.
DOI : 10.1109/ICPR.2016.7900038

A. Serna, B. Marcotegui, F. Goulette, and J. E. Deschaud, Paris-rue-Madame database: A 3D mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods, Proceedings of the International Conference on Pattern Recognition Applications and Methods, pp.6-8, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00963812

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

J. Zhang, X. Lin, and X. Ning, SVM-based classification of segmented airborne lidar point clouds in urban areas. Remote Sens, pp.3749-3775, 2013.

A. K. Aijazi, P. Checchin, and L. Trassoudaine, Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation, Remote Sensing, vol.63, issue.4, pp.1624-1650, 2013.
DOI : 10.1016/j.isprsjprs.2007.07.005

B. Wu, B. Yu, W. Yue, S. Shu, W. Tan et al., A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data, Remote Sensing, vol.31, issue.2, pp.584-611, 2013.
DOI : 10.1080/01431160902882561

W. Yao and H. Fan, Automated detection of 3D individual trees along urban road corridors by mobile laser scanning systems, Proceedings of the International Symposium on Mobile Mapping Technology, pp.1-3, 2013.

J. Reitberger, C. Schnörr, and P. Krzystek, 3D segmentation of single trees exploiting full waveform LIDAR data, ISPRS Journal of Photogrammetry and Remote Sensing, vol.64, issue.6, pp.561-574, 2009.
DOI : 10.1016/j.isprsjprs.2009.04.002

M. Rutzinger, A. K. Pratihast, S. Oude-elberink, G. Vosselman, A. K. Pratihast et al., Detection and modelling of 3D trees from mobile laser scanning data Tree modelling from mobile laser scanning data-sets, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, vol.2010, issue.26, pp.361-372

F. Monnier, B. Vallet, and B. Soheilian, TREES DETECTION FROM LASER POINT CLOUDS ACQUIRED IN DENSE URBAN AREAS BY A MOBILE MAPPING SYSTEM, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, pp.3-245
DOI : 10.5194/isprsannals-I-3-245-2012

O. Elberink, S. Kemboi, and B. , User-assisted Object Detection by Segment Based Similarity Measures in Mobile Laser Scanner Data, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, pp.3-239
DOI : 10.5194/isprsarchives-XL-3-239-2014

S. Gupta, H. Weinacker, and B. Koch, Comparative analysis of clustering-based approaches for 3-D single tree detection using airborne fullwave lidar data. Remote Sens, pp.968-989, 2010.

K. Fukunaga and L. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory, vol.21, issue.1, pp.32-40, 1975.
DOI : 10.1109/TIT.1975.1055330

A. Ferraz, F. Bretar, S. Jacquemoud, G. Goncalves, L. Pereira et al., 3-D mapping of a multi-layered Mediterranean forest using ALS data, Remote Sensing of Environment, vol.121, pp.210-223
DOI : 10.1016/j.rse.2012.01.020

M. Schmitt, A. Brück, J. Schönberger, and U. Stilla, Potential of airborne single-pass millimeterwave InSAR data for individual tree recognition, Proceedings of the Tagungsband der Dreiländertagung der DGPF, der OVG und der SGPF, pp.427-436, 2013.

W. Yao, P. Krzystek, and M. Heurich, Enhanced detection of 3D individual trees in forested areas using airborne full-waveform LiDAR data by combining normalized cuts with spatial density clustering, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.5, issue.2, pp.5-349
DOI : 10.5194/isprsannals-II-5-W2-349-2013

M. Shahzad, M. Schmitt, and X. Zhu, SEGMENTATION AND CROWN PARAMETER EXTRACTION OF INDIVIDUAL TREES IN AN AIRBORNE TOMOSAR POINT CLOUD, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, issue.2, pp.205-209
DOI : 10.5194/isprsarchives-XL-3-W2-205-2015

M. Schmitt, M. Shahzad, and X. Zhu, Reconstruction of individual trees from multi-aspect TomoSAR data, Remote Sensing of Environment, vol.165, pp.175-185, 2015.
DOI : 10.1016/j.rse.2015.05.012

Z. Zhao, F. Morstatter, S. Sharma, S. Alelyani, A. Anand et al., Advancing Feature Selection Research ? ASU Feature Selection Repository Correlation-based feature subset selection for machine learning Numerical recipes in C, Report; School of Computing, Informatics, and Decision Systems Engineering, 1988.

L. Yu and H. Liu, Feature selection for high-dimensional data: A fast correlation-based filter solution, Proceedings of the International Conference on Machine Learning, pp.21-24, 2003.

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis
DOI : 10.1007/978-1-4471-4929-3

M. Weinmann, C. Mallet, and M. Brédif, Segmentation and localization of individual trees from MMS point cloud data acquired in urban areas, Proceedings of the Tagungsband der Dreiländertagung der DGPF, der OVG und der SGPF, pp.7-9, 2016.

L. Caraffa, M. Brédif, and B. Vallet, 3D OCTREE BASED WATERTIGHT MESH GENERATION FROM UBIQUITOUS DATA, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, issue.3, pp.613-617
DOI : 10.5194/isprsarchives-XL-3-W3-613-2015
URL : http://doi.org/10.5194/isprsarchives-xl-3-w3-613-2015

Y. Cheng, Mean shift, mode seeking, and clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.8, pp.790-799, 1995.
DOI : 10.1109/34.400568

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236
URL : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.160.3832&rep=rep1&type=pdf

C. Chen, A. Liaw, and L. Breiman, Using Random Forest to Learn Imbalanced Data, 2004.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res, vol.3, pp.1157-1182, 2003.

S. Soudarissanane, R. Lindenbergh, M. Menenti, P. Teunissen, and B. Jutzi, Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points Geometric point quality assessment for the automated, markerless and robust registration of unordered TLS point clouds, ISPRS J. Photogramm. Remote Sens ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, vol.66, issue.2015, pp.389-399, 2011.

J. Böhm, M. Brédif, T. Gierlinger, M. Krämer, R. Lindenbergh et al., The IQmulus urban showcase: Automatic tree classification and identification in huge mobile mapping point clouds, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, vol.2016, pp.3-301

S. Wang, M. Bai, G. Mattyus, H. Chu, W. Luo et al., Seeing the world with a million eyes. arXiv 2016, arXiv:1612.00423. c 2017 by the authors, This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license