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Havasal LiDAR Nokta Bulutlarından Kentsel Yol Ağlarının Çıkarımı, Bergama Test Alanı

Year 2022, , 53 - 59, 31.12.2022
https://doi.org/10.51946/melid.1170411

Abstract

Kentsel alanlarda ulaşımın en temel faktörü karayoludur. Karayolları kent içinde sürekli bir kullanım alanı olduğundan sürdürülebilir ve izlenebilir bir yapıda olmalıdır. Kentsel alanlarda yer alan yolların envanteri, proje ve planlarının temin edilmesi akıllı şehirlerin üretilmesi açısından önemli bir altyapıyı oluşturur. Navigasyon amaçlı kullanımın yanında kent planlarının üretilmesinde güncel durum tespiti açısından yol ağlarının haritalandırılması gerekir. Yersel ölçmelerle yol ağı ölçmeleri zahmetli, zaman alıcı ve ekonomik külfeti yüksektir. Gelişen teknolojiyle geleneksel ve yersel ölçme tekniklerine alternatif etkili ve geniş alanların haritalandırılmasına katkı sağlayan ölçme tekniklerinin kullanılması işlemleri kolaylaştırmaktadır. Bu çalışmada yol ağlarının yarı otomatik çıkarımı için bir metodoloji önerilmiştir. Önerilen metodolojide, ham Havasal LiDAR nokta bulutlarının ön işlemesi, yükseklik sapmalarının hesaplanması, düzlemsellik ve bağlantılı bileşen analizlerinden faydalanılmıştır. Bazı parametrelerin kullanıcı tarafından deneme yanılma yöntemiyle girilmesi nedeniyle yöntem yarı otomatik olarak çalışmaktadır. Çalışma alanı Harita Genel Müdürlüğü tarafından Havasal LiDAR ölçümlerinin gerçekleştirildiği Bergama test alanıdır. Elde edilen sonuçlar manuel çizimlerle görsel olarak karşılaştırılmış ve önemli oranlarda örtüşme sağlayan sonuçlar elde edilmiştir.

Thanks

Makalede kullanılan test verisi Harita Genel Müdürlüğü’nün Bergama test verisinin bir bölümünü içermektedir. Bu çalışmanın ortaya çıkarılmasında veri paylaşımında bulunan kuruma teşekkürlerimi sunarım.

References

  • Antah, F. H., Khoiry, M. A., Maulud, K. N. A. & Ibrahim, A. N. H. (2022). Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia. Sustainability, 14(15).
  • Biçici, S. & Zeybek, M. (2021). An approach for the automated extraction of road surface distress from a UAV-derived point cloud. Automation in Construction, 122, 103475. doi:10.1016/j.autcon.2020.103475
  • Cao, L., Wang, Y. & Liu, C. (2021). Study of unpaved road surface erosion based on terrestrial laser scanning. Catena, 199. doi:10.1016/j.catena.2020.105091
  • Eddelbuettel, D. & Sanderson, C. (2014). RcppArmadillo: Accelerating R with high-performance C++ linear algebra. Computational Statistics & Data Analysis, 71, 1054-1063. doi:10.1016/j.csda.2013.02.005
  • Gargoum, S. A., El-Basyouny, K., Froese, K. & Gadowski, A. (2018). A Fully Automated Approach to Extract and Assess Road Cross Sections From Mobile LiDAR Data. Ieee Transactions on Intelligent Transportation Systems, 19(11), 3507-3516. doi:10.1109/Tits.2017.2784623
  • Gargoum, S., Karsten, L., El-Basyouny, K. & Chen, X. (2022). Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis. Infrastructures, 7(1). doi:10.3390/infrastructures7010007
  • Girardeau-Montaut, D. (2019). Cloudcompare GPL software version 2.10. Erişim Linki https://www.danielgm.net/cc/. Erişim tarihi: 08 December 2020
  • Gombin, J., Vaidyanathan, R. & Agafonkin, V. (2020). concaveman: A Very Fast 2D Concave Hull Algorithm (Version R package version 1.1.0). Retrieved from https://CRAN.R-project.org/package=concaveman
  • Isenburg, M. (2021). LAStools—Efficient tools for LiDAR processing.
  • Kayı, A., Erdoğan, M. & Eker, O. (2015). OPTECH HA-500 ve RIEGL LMS-Q1560 ile Gerçekleştirilen LiDAR Test Sonuçları. Harita Dergisi, 153.
  • Liu, L. & Lim, S. (2016). A framework of road extraction from airborne lidar data and aerial imagery. Journal of Spatial Science, 61(2), 263-281. doi:10.1080/14498596.2016.1147392
  • Ma, H. C., Ma, H. C., Zhang, L., Liu, K. & Luo, W. J. (2022). Extracting Urban Road Footprints from Airborne LiDAR Point Clouds with PointNet plus plus and Two-Step Post-Processing. Remote Sensing, 14(3).
  • McManamon, P. F. (2019). LiDAR Technologies and Systems.
  • Olsen, M. J., Roe, G. V., Glennie, C., Persi, F., Reedy, M., David Hurwitz, . . . Knodler, M. (2013). Guidelines for the Use of Mobile LIDAR in Transportation Applications. Retrieved from
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  • Roussel, J. R., Auty, D., De Boissieu, F., Meador, A. S. & Bourdon, J. F. (2018). lidR: Airborne LiDAR data manipulation and visualization for forestry applications. R package version, 1(1).
  • Rusu, R. B. & Cousins, S. (2011). Point cloud library (pcl). 2011 IEEE international conference on robotics and automation.
  • Sameen, M. I. & Pradhan, B. (2016). A Simplified Semi-Automatic Technique for Highway Extraction from High-Resolution Airborne LiDAR Data and Orthophotos. Journal of the Indian Society of Remote Sensing, 45(3), 395-405. doi:10.1007/s12524-016-0610-5
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  • Team, R. C. (2021). R: A Language and Environment for Statistical Computing. Erişim Linki https://cran.r-project.org/. Erişim tarihi: 13/10/2021
  • Vosselman, G. & Maas, H. G. (2010). Airborne and Terrestrial Laser Scanning: Whittles Publishing.
  • Wang, J., Hu, Z. Q., Chen, Y. Y. & Zhang, Z. Q. (2017). Automatic Estimation of Road Slopes and Superelevations Using Point Clouds. Photogrammetric Engineering and Remote Sensing, 83(3), 217-223. doi:10.14358/Pers.83.3.217
  • Wang, R. S., Peethambaran, J. & Chen, D. (2018). LiDAR Point Clouds to 3-D Urban Models: A Review. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 606-627. doi:10.1109/Jstars.2017.2781132
  • Wei, C. T., Tsai, M. D., Chang, Y. L. & Wang, M. C. J. (2022). Enhancing the Accuracy of Land Cover Classification by Airborne LiDAR Data and WorldView-2 Satellite Imagery. ISPRS International Journal of Geo-Information, 11(7).
  • Yadav, M., Khan, P., Singh, A. K. & Lohani, B. (2021). An automatic hybrid method for ground filtering in mobile laser scanning data of various types of roadway environments. Automation in Construction, 126.
  • Yermo, M., Rivera, F. F., Cabaleiro, J. C., Vilarino, D. L. & Pena, T. F. (2022). A fast and optimal pathfinder using airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 482-495.
  • Zeybek, M. (2021). Inlier Point Preservation in Outlier Points Removed from the ALS Point Cloud. Journal of the Indian Society of Remote Sensing, 49(10), 2347-2363. doi:10.1007/s12524-021-01397-4
  • Zhang, W. M., Qi, J. B., Wan, P., Wang, H. T., Xie, D. H., Wang, X. Y. & Yan, G. J. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. doi:10.3390/rs8060501
  • Zhu, J. F., Sui, L. C., Zang, Y. F., Zheng, H., Jiang, W., Zhong, M. Q. & Ma, F. (2021). Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network. ISPRS International Journal of Geo-Information, 10(7).

Extraction of urban road networks from aerial LiDAR point clouds, Bergama test site

Year 2022, , 53 - 59, 31.12.2022
https://doi.org/10.51946/melid.1170411

Abstract

The essential factor of transportation in urban areas is the highway. Since the highways are a continuous usage area in the city, they should be in a sustainable and traceable structure. Providing the inventory, projects, and plans of the roads in urban areas constitutes a substantial infrastructure for producing smart cities. Besides being used for navigation purposes, road networks should be mapped in order to determine the current situation in the production of city plans. Road network measurements with traditional measurements are laborious, time-consuming, and economically burdensome. With the developing technology, using measurement techniques that contribute to mapping effective and large areas as an alternative to traditional and terrestrial measurement techniques makes procedures more straightforward. In this study, a methodology for the semi-automatic extraction of road networks is proposed. The proposed methodology uses preprocessing of raw Aerial LiDAR point clouds, calculation of height deviations, planarity, and connected component analysis. The method works semi-automatically because the user enters some parameters through trial and error. The study area is the Bergama test area, where Aerial LiDAR measurements are carried out by the General Directorate of Mapping. The results obtained were compared visually with the manual drawings, and results were obtained with considerable overlap.

References

  • Antah, F. H., Khoiry, M. A., Maulud, K. N. A. & Ibrahim, A. N. H. (2022). Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia. Sustainability, 14(15).
  • Biçici, S. & Zeybek, M. (2021). An approach for the automated extraction of road surface distress from a UAV-derived point cloud. Automation in Construction, 122, 103475. doi:10.1016/j.autcon.2020.103475
  • Cao, L., Wang, Y. & Liu, C. (2021). Study of unpaved road surface erosion based on terrestrial laser scanning. Catena, 199. doi:10.1016/j.catena.2020.105091
  • Eddelbuettel, D. & Sanderson, C. (2014). RcppArmadillo: Accelerating R with high-performance C++ linear algebra. Computational Statistics & Data Analysis, 71, 1054-1063. doi:10.1016/j.csda.2013.02.005
  • Gargoum, S. A., El-Basyouny, K., Froese, K. & Gadowski, A. (2018). A Fully Automated Approach to Extract and Assess Road Cross Sections From Mobile LiDAR Data. Ieee Transactions on Intelligent Transportation Systems, 19(11), 3507-3516. doi:10.1109/Tits.2017.2784623
  • Gargoum, S., Karsten, L., El-Basyouny, K. & Chen, X. (2022). Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis. Infrastructures, 7(1). doi:10.3390/infrastructures7010007
  • Girardeau-Montaut, D. (2019). Cloudcompare GPL software version 2.10. Erişim Linki https://www.danielgm.net/cc/. Erişim tarihi: 08 December 2020
  • Gombin, J., Vaidyanathan, R. & Agafonkin, V. (2020). concaveman: A Very Fast 2D Concave Hull Algorithm (Version R package version 1.1.0). Retrieved from https://CRAN.R-project.org/package=concaveman
  • Isenburg, M. (2021). LAStools—Efficient tools for LiDAR processing.
  • Kayı, A., Erdoğan, M. & Eker, O. (2015). OPTECH HA-500 ve RIEGL LMS-Q1560 ile Gerçekleştirilen LiDAR Test Sonuçları. Harita Dergisi, 153.
  • Liu, L. & Lim, S. (2016). A framework of road extraction from airborne lidar data and aerial imagery. Journal of Spatial Science, 61(2), 263-281. doi:10.1080/14498596.2016.1147392
  • Ma, H. C., Ma, H. C., Zhang, L., Liu, K. & Luo, W. J. (2022). Extracting Urban Road Footprints from Airborne LiDAR Point Clouds with PointNet plus plus and Two-Step Post-Processing. Remote Sensing, 14(3).
  • McManamon, P. F. (2019). LiDAR Technologies and Systems.
  • Olsen, M. J., Roe, G. V., Glennie, C., Persi, F., Reedy, M., David Hurwitz, . . . Knodler, M. (2013). Guidelines for the Use of Mobile LIDAR in Transportation Applications. Retrieved from
  • QGIS Association. Retrieved from https://www.qgis.org/
  • Roussel, J. R., Auty, D., De Boissieu, F., Meador, A. S. & Bourdon, J. F. (2018). lidR: Airborne LiDAR data manipulation and visualization for forestry applications. R package version, 1(1).
  • Rusu, R. B. & Cousins, S. (2011). Point cloud library (pcl). 2011 IEEE international conference on robotics and automation.
  • Sameen, M. I. & Pradhan, B. (2016). A Simplified Semi-Automatic Technique for Highway Extraction from High-Resolution Airborne LiDAR Data and Orthophotos. Journal of the Indian Society of Remote Sensing, 45(3), 395-405. doi:10.1007/s12524-016-0610-5
  • Team, Q. D. (2022). QGIS Geographic Information System
  • Team, R. C. (2021). R: A Language and Environment for Statistical Computing. Erişim Linki https://cran.r-project.org/. Erişim tarihi: 13/10/2021
  • Vosselman, G. & Maas, H. G. (2010). Airborne and Terrestrial Laser Scanning: Whittles Publishing.
  • Wang, J., Hu, Z. Q., Chen, Y. Y. & Zhang, Z. Q. (2017). Automatic Estimation of Road Slopes and Superelevations Using Point Clouds. Photogrammetric Engineering and Remote Sensing, 83(3), 217-223. doi:10.14358/Pers.83.3.217
  • Wang, R. S., Peethambaran, J. & Chen, D. (2018). LiDAR Point Clouds to 3-D Urban Models: A Review. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 606-627. doi:10.1109/Jstars.2017.2781132
  • Wei, C. T., Tsai, M. D., Chang, Y. L. & Wang, M. C. J. (2022). Enhancing the Accuracy of Land Cover Classification by Airborne LiDAR Data and WorldView-2 Satellite Imagery. ISPRS International Journal of Geo-Information, 11(7).
  • Yadav, M., Khan, P., Singh, A. K. & Lohani, B. (2021). An automatic hybrid method for ground filtering in mobile laser scanning data of various types of roadway environments. Automation in Construction, 126.
  • Yermo, M., Rivera, F. F., Cabaleiro, J. C., Vilarino, D. L. & Pena, T. F. (2022). A fast and optimal pathfinder using airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 482-495.
  • Zeybek, M. (2021). Inlier Point Preservation in Outlier Points Removed from the ALS Point Cloud. Journal of the Indian Society of Remote Sensing, 49(10), 2347-2363. doi:10.1007/s12524-021-01397-4
  • Zhang, W. M., Qi, J. B., Wan, P., Wang, H. T., Xie, D. H., Wang, X. Y. & Yan, G. J. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. doi:10.3390/rs8060501
  • Zhu, J. F., Sui, L. C., Zang, Y. F., Zheng, H., Jiang, W., Zhong, M. Q. & Ma, F. (2021). Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network. ISPRS International Journal of Geo-Information, 10(7).
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Articles
Authors

Mustafa Zeybek 0000-0001-8640-1443

Publication Date December 31, 2022
Submission Date September 2, 2022
Published in Issue Year 2022

Cite

APA Zeybek, M. (2022). Havasal LiDAR Nokta Bulutlarından Kentsel Yol Ağlarının Çıkarımı, Bergama Test Alanı. Türkiye Lidar Dergisi, 4(2), 53-59. https://doi.org/10.51946/melid.1170411

Türkiye LiDAR Dergisi