Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1105–1111, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1105-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1105–1111, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1105-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 Jun 2019

05 Jun 2019

CLASSIFICATION OF AERIAL LASER SCANNING POINT CLOUDS USING MACHINE LEARNING: A COMPARISON BETWEEN RANDOM FOREST AND TENSORFLOW

F. Pirotti1,2 and F. Tonion1,2 F. Pirotti and F. Tonion
  • 1CIRGEO - Interdepartmental Research Center of Geomatics, University of Padova, Viale dell’Università 16, Legnaro (PD), Italy
  • 2TESAF Department, University of Padova, Viale dell’Università 16, Legnaro (PD), Italy

Keywords: Deep Learning, Classification, Tensorflow, Laser Scanning, Airborne Lidar, Random Forest

Abstract. In this investigation a comparison between two machine learning (ML) models for semantic classification of an aerial laser scanner point cloud is presented. One model is Random Forest (RF), the other is a multi-layer neural network, TensorFlow (TF). Accuracy results were compared over a growing set of training data, using a stratified independent sampling over classes from 5% to 50% of the total dataset. Results show RF to have average F1 = 0.823 for the 9 classes considered, whereas TF had average F1 = 0.450. F1 values where higher for RF than TF, due to complexity in the determination of a suitable composition of the hidden layers of the neural network in TF, and this can likely be improved to reach higher accuracy values. Further study in this sense is planned.