Research Article
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Year 2020, Volume: 4 Issue: 3, 113 - 122, 01.07.2020
https://doi.org/10.31127/tuje.641501

Abstract

References

  • Akçay, Ö , Erenoğlu, R , Avşar, E (2017). The Effect Of Jpeg Compression In Close Range Photogrammetry. International Journal of Engineering and Geosciences , 2 (1) , 35-40 DOI: 10.26833/Ijeg.287308
  • Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing 33 (B4/1): 110–117.
  • Büyüksalih İ., Gazioglu C (2019). New Approach in Integrated Basin Modelling: Melen Airborne LIDAR. International Journal of Environment and Geoinformatics (IJEGEO) 6: 22-32. 10.30897/ijegeo.530272.
  • Canaz S, Habib A (2013) Photogrammetric features for the registration of terrestrial laser scans with minimum overlap. J. Geod. Geoinf. 2013, 2, 1–8.
  • Canaz Sevgen, S . (2019). Airborne Lidar Data Classification In Complex Urban Area Using Random Forest: A Case Study of Bergama, Turkey. International Journal of Engineering and Geosciences , 4 (1) , 45-51 .doi: 10.26833/ijeg.440828
  • Chen Z, Gao B, Devereux B (2017) State-of-the-art:DTM generation using airborne LIDAR data. Sensors 2017, 17, 150.
  • Chen Q, Gong P, Baldocchi D, Xin G (2007). Filtering airborne laser scanning data with morphological methods. Photogrammetric Engineering and Remote Sensing 73(2), 175-185.
  • Filin S, Pfeifer N (2006) Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS Journal of Photogrammetry and Remote Sensing 60, 71–80.
  • Kilian J, Haala N, Englich M (1996) Capture and evaluation of airborne laser scanner data, International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences 31(B3), 383-388.
  • Kraus K, Pfeifer N (2001) Advanced DTM generation from LIDAR data. International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, XXXIV (Pt. 3/W4) (2001), pp. 23-30.
  • Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 53(4), 193-203.
  • LAStools (2017) Award-winning software for efficient LiDAR processing with LASzip, obtained from https://rapidlasso.com/LAStools/
  • Lee HS, Younan NH (2003) DTM extraction of LiDAR returns via adaptive processing. IEEE Transactions on Geoscience and Remote Sensing 41(9), 2063-2069.
  • Liu X, Zhang Z (2008) Lidar data reduction for efficient and high quality DEM generation. Int. Arch. Photogram. Remote Sens. Spat. Inform. Sci. vol. 37 (pg. 173 -178 ).
  • Liu X (2008) Airborne LiDAR for DEM generation: some critical issues. Prog. Phys. Geog. 32, 31-49.
  • Liu XY (2011) Accuracy assessment of LiDAR elevation data using survey marks. Surv Rev 43:80–93. doi: 10.1179/003962611X12894696204704
  • Lohmann P, Koch A, Schaeffer M (200) Approaches to the filtering of laser scanner data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 33(B3), 540-547.
  • Mongus D, Žalik B (2012) Parameter-free ground filtering of LiDAR data for automatic DTM generation. ISPRS J. Photogrammetry 67, 1–12
  • Mongus D, Lukač N, Žalik B (2014) Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces. ISPRS J. Photogramm. Remote Sens. Vol; 93, 145–156.
  • Rashidi P, Rastiveis H, (2017) Ground Filtering Lidar Data Based On Multi-Scale Analysis Of Height Difference Threshold. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W4, 2017
  • Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–10 October 2017, Tehran, Iran
  • Shan J, Sampath A (2005) Urban DEM generation from raw LiDAR data: a labeling algorthm and its performance. Photogrammetric Engineering and Remote Sensing 71, 217-26
  • Tovari D, Pfeifer N (2005) Segmentation based robust interpolation – a new approach to laser data filtering. IAPRS Vol XXXVI, 3/W3, Enschede, the Netherlands.
  • Uysal, M. Polat, N (2014) Investigating Performance Of Airborne Lidar Data Filtering With Triangular Irregular Network (TIN) Algorithm, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 199-202, https://doi.org/10.5194/isprsarchives-XL-7-199-2014, 2014.
  • Vosselman G (2000) Slope based filtering of laser altimetry data, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 33(part B3/2), 935-934.
  • Wang CK, Tseng YH (2010) DEM generation from airborne LiDAR data by adaptive dual-directional slope filter. Int. Arch. Photogram. Remote Sens. Spat. Inform. Sci 38(Part 7B), 628–632.
  • Yuan F, Zhang J X, Zhang L, Gao J (2009) DEM generation from airborne LIDAR data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol XXXVIII-7/C4 : 308-312
  • Yuan F, Zhang J, Zhang L (2009) Urban DEM generation from airborne Lidar data[C]. Urban Remote Sensing Event, 2009 Joint. IEEE, pp 1–5.
  • Zakšek K, Pfeifer N (2006) An improved morphological filter for selecting relief points from a LiDAR point cloud in steep areas with dense vegetation, Luubljana, Slovenia and Innsbruck, Austria: Institute of Anthropological and Spatial Studies, Scientific Research Centre of the Slovenian Academy of Sciences and Arts, and Institute of Geography, Innsbruck University.
  • Zhang Y, Wang L (2016) Computer Vision and Pattern Recognition LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation. Cornell University Computer Vision and Pattern Recognition
  • Zhang KQ, Chen SC, Whitman D, Shyu ML, Yan JH, Zhang CC (2003) A progressive morphological filter for removing nonground measurements from airborne LiDAR data, IEEE Transactions on Geoscience and Remote Sensing 41(4), 872-882.
  • Zhang WM, Qi JB, Wan P, Wang HT, Xie DH, Wang XY, Yan GJ (2016) An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens. 2016, 8, 501
  • Zeybek M., Şanlıoglu İ., Genc A (2015) Yüksek Çözünürlüklü Yersel lazer tarama verilerinin filtrelenmesi ve filtrelemelerin heyelan izlemeye etkisi. Dogal Afetler ve Cevre Dergisi, 1:11 – 20, 2015.
  • Zeybek M., Şanlıoğlu İ (2019) Point cloud filtering on UAV based point cloud. Measurement 133, 99-111.
  • Yilmaz Serifoglu C, Yilmaz V, Gungor O (2018) Investigating the performances of commercial and noncommercial software for ground filtering of UAV-based point clouds. International Journal of Remote Sensing.
  • Yilmaz, M , Uysal, M (2017). Comparing Uniform And Random Data Reduction Methods For Dtm Accuracy. International Journal of Engineering and Geosciences , 2 (1) , 9-16 . doi: 10.26833/ijeg.286003
  • Wallace L, Lucieer A, Malenovský Z, Turner D, Vopěnka P (2016) Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (Sfm) Point Clouds Forests. 7 (3): 62. doi:10.3390/f7030062.
  • Wang Q, Wu L, Wu Z, Tang H, Wang R, Li F (2014) A progressive morphological filter for point cloud extracted from UAV images.” In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) doi: 10.1109/ IGARSS. 2014. 6946860.

AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA

Year 2020, Volume: 4 Issue: 3, 113 - 122, 01.07.2020
https://doi.org/10.31127/tuje.641501

Abstract

Terrain models play a key role in many applications, such as hydrological modeling, volume calculation, wire and pipeline route planning as well as many engineering applications. While terrain models can be generated from traditional data sources, an advanced and recently popular geospatial technology, Light Detection and Ranging (LiDAR) data, is also a source for generating high-density terrain models in the last decades. The main advantage of LiDAR technology over traditional data sources is that it generates 3D point clouds directly so that the representation of the surfaces is obtained fast. On the other hand, before terrain modeling, ground points need to be extracted by point labeling in the 3D point cloud. In this study, a new algorithm is proposed for automatic ground point extraction from airborne LiDAR data for urban areas. The proposed algorithm is mainly based on height information of the points in the dataset and labels ground points comparing height differences in local windows. The algorithm does not require any user input threshold and a neighborhood definition. The proposed ground extraction algorithm was tested with three different urban area LiDAR data. The quality control basically performed qualitatively by visual inspection and quantitatively by calculation of overall accuracy, which is conduct by comparing the proposed algorithm results with data provider’s ground classification and Cloth Simulation Filtering (CSF) algorithm’s results. The overall accuracy of the proposed algorithm is found between 95%-98%. The experimental results showed that the algorithm promises reliable results to extract ground points from airborne LiDAR data for urban areas. 

References

  • Akçay, Ö , Erenoğlu, R , Avşar, E (2017). The Effect Of Jpeg Compression In Close Range Photogrammetry. International Journal of Engineering and Geosciences , 2 (1) , 35-40 DOI: 10.26833/Ijeg.287308
  • Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing 33 (B4/1): 110–117.
  • Büyüksalih İ., Gazioglu C (2019). New Approach in Integrated Basin Modelling: Melen Airborne LIDAR. International Journal of Environment and Geoinformatics (IJEGEO) 6: 22-32. 10.30897/ijegeo.530272.
  • Canaz S, Habib A (2013) Photogrammetric features for the registration of terrestrial laser scans with minimum overlap. J. Geod. Geoinf. 2013, 2, 1–8.
  • Canaz Sevgen, S . (2019). Airborne Lidar Data Classification In Complex Urban Area Using Random Forest: A Case Study of Bergama, Turkey. International Journal of Engineering and Geosciences , 4 (1) , 45-51 .doi: 10.26833/ijeg.440828
  • Chen Z, Gao B, Devereux B (2017) State-of-the-art:DTM generation using airborne LIDAR data. Sensors 2017, 17, 150.
  • Chen Q, Gong P, Baldocchi D, Xin G (2007). Filtering airborne laser scanning data with morphological methods. Photogrammetric Engineering and Remote Sensing 73(2), 175-185.
  • Filin S, Pfeifer N (2006) Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS Journal of Photogrammetry and Remote Sensing 60, 71–80.
  • Kilian J, Haala N, Englich M (1996) Capture and evaluation of airborne laser scanner data, International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences 31(B3), 383-388.
  • Kraus K, Pfeifer N (2001) Advanced DTM generation from LIDAR data. International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, XXXIV (Pt. 3/W4) (2001), pp. 23-30.
  • Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 53(4), 193-203.
  • LAStools (2017) Award-winning software for efficient LiDAR processing with LASzip, obtained from https://rapidlasso.com/LAStools/
  • Lee HS, Younan NH (2003) DTM extraction of LiDAR returns via adaptive processing. IEEE Transactions on Geoscience and Remote Sensing 41(9), 2063-2069.
  • Liu X, Zhang Z (2008) Lidar data reduction for efficient and high quality DEM generation. Int. Arch. Photogram. Remote Sens. Spat. Inform. Sci. vol. 37 (pg. 173 -178 ).
  • Liu X (2008) Airborne LiDAR for DEM generation: some critical issues. Prog. Phys. Geog. 32, 31-49.
  • Liu XY (2011) Accuracy assessment of LiDAR elevation data using survey marks. Surv Rev 43:80–93. doi: 10.1179/003962611X12894696204704
  • Lohmann P, Koch A, Schaeffer M (200) Approaches to the filtering of laser scanner data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 33(B3), 540-547.
  • Mongus D, Žalik B (2012) Parameter-free ground filtering of LiDAR data for automatic DTM generation. ISPRS J. Photogrammetry 67, 1–12
  • Mongus D, Lukač N, Žalik B (2014) Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces. ISPRS J. Photogramm. Remote Sens. Vol; 93, 145–156.
  • Rashidi P, Rastiveis H, (2017) Ground Filtering Lidar Data Based On Multi-Scale Analysis Of Height Difference Threshold. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W4, 2017
  • Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–10 October 2017, Tehran, Iran
  • Shan J, Sampath A (2005) Urban DEM generation from raw LiDAR data: a labeling algorthm and its performance. Photogrammetric Engineering and Remote Sensing 71, 217-26
  • Tovari D, Pfeifer N (2005) Segmentation based robust interpolation – a new approach to laser data filtering. IAPRS Vol XXXVI, 3/W3, Enschede, the Netherlands.
  • Uysal, M. Polat, N (2014) Investigating Performance Of Airborne Lidar Data Filtering With Triangular Irregular Network (TIN) Algorithm, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 199-202, https://doi.org/10.5194/isprsarchives-XL-7-199-2014, 2014.
  • Vosselman G (2000) Slope based filtering of laser altimetry data, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 33(part B3/2), 935-934.
  • Wang CK, Tseng YH (2010) DEM generation from airborne LiDAR data by adaptive dual-directional slope filter. Int. Arch. Photogram. Remote Sens. Spat. Inform. Sci 38(Part 7B), 628–632.
  • Yuan F, Zhang J X, Zhang L, Gao J (2009) DEM generation from airborne LIDAR data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol XXXVIII-7/C4 : 308-312
  • Yuan F, Zhang J, Zhang L (2009) Urban DEM generation from airborne Lidar data[C]. Urban Remote Sensing Event, 2009 Joint. IEEE, pp 1–5.
  • Zakšek K, Pfeifer N (2006) An improved morphological filter for selecting relief points from a LiDAR point cloud in steep areas with dense vegetation, Luubljana, Slovenia and Innsbruck, Austria: Institute of Anthropological and Spatial Studies, Scientific Research Centre of the Slovenian Academy of Sciences and Arts, and Institute of Geography, Innsbruck University.
  • Zhang Y, Wang L (2016) Computer Vision and Pattern Recognition LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation. Cornell University Computer Vision and Pattern Recognition
  • Zhang KQ, Chen SC, Whitman D, Shyu ML, Yan JH, Zhang CC (2003) A progressive morphological filter for removing nonground measurements from airborne LiDAR data, IEEE Transactions on Geoscience and Remote Sensing 41(4), 872-882.
  • Zhang WM, Qi JB, Wan P, Wang HT, Xie DH, Wang XY, Yan GJ (2016) An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens. 2016, 8, 501
  • Zeybek M., Şanlıoglu İ., Genc A (2015) Yüksek Çözünürlüklü Yersel lazer tarama verilerinin filtrelenmesi ve filtrelemelerin heyelan izlemeye etkisi. Dogal Afetler ve Cevre Dergisi, 1:11 – 20, 2015.
  • Zeybek M., Şanlıoğlu İ (2019) Point cloud filtering on UAV based point cloud. Measurement 133, 99-111.
  • Yilmaz Serifoglu C, Yilmaz V, Gungor O (2018) Investigating the performances of commercial and noncommercial software for ground filtering of UAV-based point clouds. International Journal of Remote Sensing.
  • Yilmaz, M , Uysal, M (2017). Comparing Uniform And Random Data Reduction Methods For Dtm Accuracy. International Journal of Engineering and Geosciences , 2 (1) , 9-16 . doi: 10.26833/ijeg.286003
  • Wallace L, Lucieer A, Malenovský Z, Turner D, Vopěnka P (2016) Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (Sfm) Point Clouds Forests. 7 (3): 62. doi:10.3390/f7030062.
  • Wang Q, Wu L, Wu Z, Tang H, Wang R, Li F (2014) A progressive morphological filter for point cloud extracted from UAV images.” In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) doi: 10.1109/ IGARSS. 2014. 6946860.
There are 38 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Sibel Canaz Sevgen 0000-0001-5552-6067

Fevzi Karsli This is me 0000-0002-0411-3315

Publication Date July 1, 2020
Published in Issue Year 2020 Volume: 4 Issue: 3

Cite

APA Canaz Sevgen, S., & Karsli, F. (2020). AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA. Turkish Journal of Engineering, 4(3), 113-122. https://doi.org/10.31127/tuje.641501
AMA Canaz Sevgen S, Karsli F. AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA. TUJE. July 2020;4(3):113-122. doi:10.31127/tuje.641501
Chicago Canaz Sevgen, Sibel, and Fevzi Karsli. “AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA”. Turkish Journal of Engineering 4, no. 3 (July 2020): 113-22. https://doi.org/10.31127/tuje.641501.
EndNote Canaz Sevgen S, Karsli F (July 1, 2020) AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA. Turkish Journal of Engineering 4 3 113–122.
IEEE S. Canaz Sevgen and F. Karsli, “AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA”, TUJE, vol. 4, no. 3, pp. 113–122, 2020, doi: 10.31127/tuje.641501.
ISNAD Canaz Sevgen, Sibel - Karsli, Fevzi. “AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA”. Turkish Journal of Engineering 4/3 (July 2020), 113-122. https://doi.org/10.31127/tuje.641501.
JAMA Canaz Sevgen S, Karsli F. AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA. TUJE. 2020;4:113–122.
MLA Canaz Sevgen, Sibel and Fevzi Karsli. “AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA”. Turkish Journal of Engineering, vol. 4, no. 3, 2020, pp. 113-22, doi:10.31127/tuje.641501.
Vancouver Canaz Sevgen S, Karsli F. AUTOMATIC GROUND EXTRACTION FOR URBAN AREAS FROM AIRBORNE LIDAR DATA. TUJE. 2020;4(3):113-22.
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