Improving CityGML LOD-1 building models using terrestrial point cloud data

Fuan Tsai, Jhe Syuan Lai, Yu Ching Liu

Research output: Contribution to conferencePaperpeer-review

Abstract

The elevation of the generated OGC CityGML LOD-1 models is usually determined as the height of the roof from ground. However, this criterion might result in significant discrepancies between the generated models and some real buildings, such as arcades and open spaces on first floors. This study develops a procedure to improve the OGC CityGML LOD-1 building models based on terrestrial point clouds derived from close-range images. The point clouds are generated from multiple photos acquired using non-metric cameras (including cell phones), and analyzed by the CMVS (Clustering Views for Multi-view Stereo) algorithm which is one of the dense matching methods for point cloud generation. Experimental results presented in this paper demonstrate the effectiveness of the developed procedure, and show that the improved LOD-1 building models are more close to real buildings in terms of shapes and appearances. In addition, this study also briefly compares the developed method with other conventional approaches (e.g., laser scanning and total station) for improving OGC CityGML LOD-1 building models.

Original languageEnglish
Pages3011-3014
Number of pages4
StatePublished - 2018
Event39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018 - Kuala Lumpur, Malaysia
Duration: 15 Oct 201819 Oct 2018

Conference

Conference39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018
Country/TerritoryMalaysia
CityKuala Lumpur
Period15/10/1819/10/18

Keywords

  • 3D building model
  • Dense matching
  • Level of detail
  • OGC CityGML
  • Point cloud.

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