Delft Windmill Interior and Exterior Laser Scanning Point Clouds - The Raw Data

Datacite citation style:
Beril Sirmacek (2015): Delft Windmill Interior and Exterior Laser Scanning Point Clouds - The Raw Data. Version 1. 4TU.ResearchData. dataset.
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Windmill "De Roos", Delft
lat (N): 52.013928
lon (E): 4.351275
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Delftse Molen or Molen de Roos Windmill laser scanning data, point clouds are acquired for BSc Thesis of Joppe Roebroeks The data includes generated mesh models from the point clouds as well. Merging interior and exterior laser scanning point clouds of the historical windmill, completing the missing parts from airborne laser scanning data and trying different mesh generation methods for full 3D windmill model generation in order to be able to perform virtual visits in the future. Background: “Molen de Roos” a mill located in Delft, a city in the western part of the Netherlands, is only one of the many old buildings in the Netherlands. More and more these buildings are degrading with respect to integrity, suffering from their age. Therefore it would be desired to look for ways to scan the building in order to find damaged areas. They can then be repaired before the building is being damaged beyond repair (or very expensively) or before even losing the entire building (through collapse). In the BSc thesis there is a focus on documentation of the precise building contour my means of laser scanning and processing the data into a 3D model. Detailed laser scanning makes it possible to map a large part of the surface area of the interior and exterior of mill in order to find flaws, direct damage or propagating damage over time. This dataset is the raw data, not processed, not reduced in size. It does not contain any correction.
  • 2015-10-19 first online, published, posted
Delft University of Technology
All data in the directories is archived in the two top level .tar.gz files. The data files are in plain text format. media types: application/octet-stream, application/x-tar-gz, text/plain
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing
  • Adriaan van Natijne
  • Ben Gorte
  • Jeroen Lichtenauer
  • Jinhu Wang
  • Joppe Roebroeks
  • Roderik Lindenbergh


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