A big data approach for comprehensive urban shadow analysis from airborne laser scanning point clouds

A. V. Vo, D. F. Laefer

Research output: Contribution to journalConference articlepeer-review


Because of the importance of access to sunlight, shadow analysis is a common consideration in urban design, especially for dense urban developments. As shadow computation is computationally expensive, most urban shadow analysis tools have to date circumvented the high computational costs by representing urban complexity only through simplified geometric models. The simplification process removes details and adversely affects the level of realism of the ultimate results. In this paper, an alternative approach is presented by utilizing the highest level of detail and resolution captured in the geometric input data source, which is an extremely high-resolution airborne laser scanning point cloud (300 points/m2). To cope with the high computational demand caused by the use of this dense and detailed input data set, the Comprehensive Urban Shadow algorithm is introduced to distribute the computation for parallel processing on a Hadoop cluster. The proposed comprehensive urban shadow analysis solution is scalable, reasonably fast, and capable of preserving the original resolution and geometric detail of the original point cloud data.

Original languageEnglish (US)
Pages (from-to)131-137
Number of pages7
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Issue number4/W8
StatePublished - 2019
Event14th 3D GeoInfo Conference 2019 - Singapore, Singapore
Duration: Sep 24 2019Sep 27 2019


  • Big Data
  • Distributed Computing
  • LiDAR
  • Point Cloud
  • Shadow Analysis
  • Urban Shadow

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Environmental Science (miscellaneous)
  • Instrumentation


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