Volume IV-4/W8
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W8, 131–137, 2019
https://doi.org/10.5194/isprs-annals-IV-4-W8-131-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W8, 131–137, 2019
https://doi.org/10.5194/isprs-annals-IV-4-W8-131-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Sep 2019

23 Sep 2019

A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS

A. V. Vo1 and D. F. Laefer1,2 A. V. Vo and D. F. Laefer
  • 1Center for Urban Science + Progress, New York University, USA
  • 2Dept. Civil and Urban Engineering, Tandon School of Engineering, New York University, USA

Keywords: Shadow Analysis, Urban Shadow, Distributed Computing, Big Data, LiDAR, Point Cloud

Abstract. 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.