Research Article
BibTex RIS Cite

Building Zone Regulation Compliance Using LIDAR Data: Real-Life Tests in İstanbul

Year 2016, Volume: 3 Issue: 1, 48 - 55, 07.03.2016
https://doi.org/10.30897/ijegeo.304428

Abstract

Airborne Laser scanning systems with light detection and ranging (LIDAR) technology is one of the fast and
accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models
(DTM/DSM) is the main application of collecting LIDAR range data. The LIDAR technique facilitates the
rapid production of models which contain ground and above surface objects in 3D (Jaafar et al., 1999). Today
automatic / semi-automatic generation of 3D digital building models and other city furniture from LIDAR data
is a very active area of scientific research (Elaksher and Bethel, 2002) indicates that manual surface
reconstruction is very costly and time consuming, and the development of automated algorithms is of great
importance. Currently with this LIDAR data in place, it is possible to derive models and information (e.g. the
heights of the buildings).This paper focuses on and explains our efforts on automatically checking Buildings’
Zoning Regulation Compliance by integrating geometric information derived from 3D LIDAR data and
semantic information acquired from 2D Implementation Development Maps

References

  • Bailang,Y. Hongxing,L Jianping,W. Yingjie,H. and Li, ,Z. 2010, Automated derivation of urban building density information using airborne LiDAR data and object-based method. Landscape and Urban Planning. 98(3-4), 210-219.
  • Elasksher, A. and Bethel, J. 2002. Building Extraction Using Lidar Data, ASORS-ACSM Annual Conference and FIG XXII Congress, Apr. 22-26, 2002.
  • Jaafar, G. Priestnall, P. and P.M. Mather, 1999. The effects of LIDAR DSM grid resolution on categorising residential and industrial buildings. Proceedings of the ISPRS Workshop, 9-11 NOVEMBER 1999, La Jolla, USA.
  • Prerna, R. and Singh, C.K. 2016, Evaluation of LiDAR and image segmentation based classification techniques for automatic building footprint extraction for a segment of Atlantic County, New Jersey, Geocarto International, Volume 31(6), pp.694-713.
  • Zhang, K. Yan, J.; Chen, S-C. 2006 Automatic Construction of Building Footprints From Airborne LIDAR Data, Geoscience and Remote Sensing, IEEE Transactions on , Vol.44, No.9, pp.2523,2533.
  • Zhao, Z., Duan, Y., Zhang, Y and Cao, R. 2015 Extracting buildings from and regularizing boundaries in airborne lidar data using connected operators, International Journal of Remote Sensing, Vol. 37(4), pp. 889-912.
  • Yu, TT., Yang, M., Chen, CS, 2005, Automatic feature extraction and stereo image processing with genetic algorithms for LIDAR data, 2nd International Conference on Computer Graphics, Imaging and Vision (CGVIS 2005), Beijing,, China, 26-29 July 2005.
  • Liu, X. 2008. Airborne LiDAR for DEM generation: some critical issues Progress in Physical Geography, Vol. 32, No. 1. pp. 31-49.
  • Cheng, L. Gong, J. Chen, X and Han, P. 2008. Building boundary extraction from high resolution imagery and LiDAR data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b.
Year 2016, Volume: 3 Issue: 1, 48 - 55, 07.03.2016
https://doi.org/10.30897/ijegeo.304428

Abstract

References

  • Bailang,Y. Hongxing,L Jianping,W. Yingjie,H. and Li, ,Z. 2010, Automated derivation of urban building density information using airborne LiDAR data and object-based method. Landscape and Urban Planning. 98(3-4), 210-219.
  • Elasksher, A. and Bethel, J. 2002. Building Extraction Using Lidar Data, ASORS-ACSM Annual Conference and FIG XXII Congress, Apr. 22-26, 2002.
  • Jaafar, G. Priestnall, P. and P.M. Mather, 1999. The effects of LIDAR DSM grid resolution on categorising residential and industrial buildings. Proceedings of the ISPRS Workshop, 9-11 NOVEMBER 1999, La Jolla, USA.
  • Prerna, R. and Singh, C.K. 2016, Evaluation of LiDAR and image segmentation based classification techniques for automatic building footprint extraction for a segment of Atlantic County, New Jersey, Geocarto International, Volume 31(6), pp.694-713.
  • Zhang, K. Yan, J.; Chen, S-C. 2006 Automatic Construction of Building Footprints From Airborne LIDAR Data, Geoscience and Remote Sensing, IEEE Transactions on , Vol.44, No.9, pp.2523,2533.
  • Zhao, Z., Duan, Y., Zhang, Y and Cao, R. 2015 Extracting buildings from and regularizing boundaries in airborne lidar data using connected operators, International Journal of Remote Sensing, Vol. 37(4), pp. 889-912.
  • Yu, TT., Yang, M., Chen, CS, 2005, Automatic feature extraction and stereo image processing with genetic algorithms for LIDAR data, 2nd International Conference on Computer Graphics, Imaging and Vision (CGVIS 2005), Beijing,, China, 26-29 July 2005.
  • Liu, X. 2008. Airborne LiDAR for DEM generation: some critical issues Progress in Physical Geography, Vol. 32, No. 1. pp. 31-49.
  • Cheng, L. Gong, J. Chen, X and Han, P. 2008. Building boundary extraction from high resolution imagery and LiDAR data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b.
There are 9 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

İsmail Büyüksalih

Publication Date March 7, 2016
Published in Issue Year 2016 Volume: 3 Issue: 1

Cite

APA Büyüksalih, İ. (2016). Building Zone Regulation Compliance Using LIDAR Data: Real-Life Tests in İstanbul. International Journal of Environment and Geoinformatics, 3(1), 48-55. https://doi.org/10.30897/ijegeo.304428