3D semantic segmentation for high-resolution aerial survey derived point clouds using deep learning (demonstration)

Haoyi Xiu, Poliyapram Vinayaraj, Kyoung Sook Kim, Ryosuke Nakamura, Wanglin Yan

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Three-dimensional (3D) Semantic segmentation of aerial derived point cloud aims at assigning each point to a semantic class such as building, tree, road, and so on. Accurate 3D-segmentation results can be used as an essential information for constructing 3D city models, for assessing the urban expansion and economical condition. However, the fine-grained semantic segmentation is a challenge in high-resolution point cloud due to irregularly distributed points unlike regular pixels of image. In this demonstration, we present a case study to apply PointNet, a novel deep learning network, to outdoor aerial survey derived point clouds by considering intensity (depth) as well as spectral information (RGB). PointNet was basically designed for indoor point cloud data based on the permutation invariance of 3D points. We firstly fuse two surveying datasets of Light Detection and ranging (LiDAR) and aerial images for generating multi-sourced aerial point clouds (RGB-DI). Then, each point of fused data is classified into a semantic class of ordinary building, public facility, apartment, factory, transportation network, park, and water by reworking PointNet. The result of our approach by using deep learning shows about 0.88 accuracy and 0.64 F-measure of semantic segmentation with the RGB-DI data we have fused. It outperforms a Support Vector Machine(SVM) approach based on geometric features of linearity, planarity, scattering, and verticality of a set of 3D points.

本文言語English
ホスト出版物のタイトル26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
編集者Li Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel
出版社Association for Computing Machinery
ページ588-591
ページ数4
ISBN(電子版)9781450358897
DOI
出版ステータスPublished - 2018 11 6
イベント26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States
継続期間: 2018 11 62018 11 9

出版物シリーズ

名前GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Other

Other26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
CountryUnited States
CitySeattle
Period18/11/618/11/9

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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