O. Majgaonkar, K. Panchal, D. Laefer, M. Stanley, Y. Zaki

Research output: Contribution to journalConference articlepeer-review


Classifying objects within aerial Light Detection and Ranging (LiDAR) data is an essential task to which machine learning (ML) is applied increasingly. ML has been shown to be more effective on LiDAR than imagery for classification, but most efforts have focused on imagery because of the challenges presented by LiDAR data. LiDAR datasets are of higher dimensionality, discontinuous, heterogenous, spatially incomplete, and often scarce. As such, there has been little examination into the fundamental properties of the training data required for acceptable performance of classification models tailored for LiDAR data. The quantity of training data is one such crucial property, because training on different sizes of data provides insight into a model's performance with differing data sets. This paper assesses the impact of training data size on the accuracy of PointNet, a widely used ML approach for point cloud classification. Subsets of ModelNet ranging from 40 to 9,843 objects were validated on a test set of 400 objects. Accuracy improved logarithmically; decelerating from 45 objects onwards, it slowed significantly at a training size of 2,000 objects, corresponding to 20,000,000 points. This work contributes to the theoretical foundation for development of LiDAR-focused models by establishing a learning curve, suggesting the minimum quantity of manually labelled data necessary for satisfactory classification performance and providing a path for further analysis of the effects of modifying training data characteristics.

Original languageEnglish (US)
Pages (from-to)101-106
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue number4/W4-2021
StatePublished - Oct 7 2021
Event16th 3D GeoInfo Conference 2021 - New York City, United States
Duration: Oct 11 2021Oct 14 2021


  • Learning Curve
  • LiDAR
  • Object Classification
  • Point Cloud
  • Training

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development


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