Calculate the Height and the Number of Floors of Buildings in Sloping Roofs Using Lidar Data and Ultracam Images

Document Type : Research Paper

Authors

1 Assistant Professor in Geography and Urban Planning, University of Tabriz, Tabriz, Iran.

2 M.Sc. in Geography and Urban Planning, University of Tabriz, Tabriz, Iran.

Abstract

The height of the building is one of the important factors in organizing the urban landscape and one of the parameters affecting the urban density. The use of modern methods and tools plays an important role in extracting the height of buildings. One of these tools is LIDAR‌‌, a relatively new technology and a rapid method for ultracam, high-precision sampling to obtain digital surface-to-surface (DSM) models. the purpose of this research is to evaluate the use of a new tool in urban planning based on extracting the height of buildings and the number of building floors, using Lidar point and ultrasound images in two stages of detecting buildings and calculating the number of floors in An area of ​​Bandar_Anzali with an area of ​​23 hectares (including 417 buildings) was classified and extracted using ARCGIS, ENVILIDAR software. All the algorithms used enabled the system to successfully extract the structures from the lidar data. The obtained data and their matching with the samples taken in the field survey show the accuracy of the extracted boundaries and classes. In general, the proposed system performs well in terms of data completeness, accuracy and consistency. According to the research findings, it can be said that lidar technology has an extraordinary ability to collect very accurate and dense samples of Ground level measurements have been provided and new dimensions of accurate building height details can be extracted automatically and efficiently from aerial weather data.

Keywords

References

fmann(2007). “An Approach to 3D Building Model Reconstruction from Airborne Laser Scanner Data Using Parameter Space Analysis and Fusion of Primitives”. Munchen University, Geodesy Commission, pp:12 .http://nosazco.com/wp-content/uploads/2019/01/3d-building.pdf
Andrew S. Huntington(2020). “InGaAs Avalanche Photodiodes for Ranging and Lidar, Woodhead Publishing Series in Electronic and Optical Materials”,pp: 45.https://www.elsevier.com/books-and-journals
Azizkhani, Mohammadreza; Ebadi, Hamid; Mokhtarzadeh, Mehdi. (2012). Automatic reconstruction of the 3D building model from aerial laser scanner data with an emphasis on the development of hyper point clustering algorithms, master's thesis, civil engineering field - photogrammetric trend mapping, Khajeh Nasiroddin Tusi University, pp. 2-3.https://www.virascience.com/thesis/656787/
Bailang Yu , Hongxing Liu ,Jianping Wu ,Yingjie Hu ,Li Zhang(2010). “Automated derivation of urban building density information using airborne LiDAR data and object-based method”, jurnal Landscape and Urban Planning, pp: 210-219.https://www.sciencedirect.com/science/article/pii/S016920461000191X
Burton, E. (2000). The Compact City: Just or Just Compact? A Preliminary Analysis, Urban Studies, Vol. 37 (11) pp: 1969-2001.https://journals.sagepub.com/doi/10.1080/00420980050162184
Brenner(2005). “Building reconstruction from images and laser scanning”. International Journal of Applied Earth Observation and Geoinformation 6, pp: 187–197. http://nosazco.com/wp-content/uploads/2019/01/3d-building.pdf
Chaouch, A., and Mari, J. L., (2006). 3D Land Seismic Surveys: Defiition of Geophysical Parameters Oil & Gas Science andTechnology – Rev. IFP , Vol. 61, No. 5, pp. 611- 630. Between photogrammetry and laser scanning ISPRS Journal of Photogrammetry and Remote Sensing 54, pp: 83–94 .http://scholar.google.com/scholar?q=:+Definition+of+Geophysical+Parameters+Oil+%26+Gas+Science+and+Technology&hl=fa&as_sdt=0&as_vis=1&oi=scholart
Cuthbert, A. R. (2006) the form of Cities, Carlton, Victoria: John Wiley, pp: 33.http://www.cmecc.com/uploads/%E8%AF%BE%E6%9C%AC%E5%92%8C%E8%AE%BA%E6%96%87/Urban.Planning_Design/[72][%E8%A7%84%E5%88%92%E8%AE%BE%E8%AE%A1]Alexander.R.Cuthbert.(2006)The.form.of.cities.political.economy.and.urban.design.pdf
Dave, Seema(2011), Neighbourhood Density and Social Sustainability in Cities of Developing Countries, Sust. Dev 19. Wiley Online Library, pp:189–205.https://onlinelibrary.wiley.com/doi/abs/10.1002/sd.433
Dong, P., Zhong, R., and Yigit, A (2018). Automated parcel-based building change detection using multitemporal airborne LiDAR data. Surveying and Land Information Science, (in press).  Surveying and Land Information Science, Volume 77, Number 1, May 2018, pp: 5-13(9).https://www.ingentaconnect.com/content/aags/salis/2018/00000077/00000001/art00002
F Ackerman(1999). “Airborne laser scanning - present status and future expectations”. J Photogramm Remote Sens 54, pp: 64–67.http://nosazco.com/wp-content/uploads/2019/01/3d-building.pdf
Fanos, A. M., Pradhan, B., Mansor, S., Yusoff, Z. M., & bin Abdullah, A. F (2018). A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data. Landslides, 15(9), pp: 1833–1850.https://link.springer.com/article/10.1007/s10346-018-0990-4
Forsyth, A., Oakes, J. M., Schmitz, K. H. & Hearst, M. (2007) Does Residential Density‌ Increase Walking and Other Physical Activity?, Urban Studies, Vol. 44 (4) ,pp: 679-‌697.‌https://journals.sagepub.com/doi/10.1080/00420980601184729
Ghorbani, Raool. (2004). Density and organization of urban spaces (example: Tabriz), PhD thesis, Tabriz University, pp. 20.https://civilica.com/doc/631747/
Hojabri, Balal; Samadzadegan, Farhad; Arefi, Hossein. (2013). Reconstruction of the building model based on the integration of lidar super point and aerial image, Scientific-Research Journal of Mapping Sciences and Techniques of the Scientific Association of Mapping and Geomatics Engineering of Iran, Scientific Association of Mapping and Geomatics Engineering of Iran, third period, number 4, pp. 103-121.https://jgst.issge.ir/browse.php?a_id=168&sid=1&slc_lang=fa
Idrees, M. O., & Pradhan, B(2018). Geostructural stability assessment of cave using rock surface discontinuity extracted from terrestrial laser scanning point cloud. Journal of Rock Mechanics and Geotechnical Engineering, 10(3), pp: 534–544.https://doi.org/10.1016/j.jrmge.2017.11.011
Iranian Statistics Center, Gilan Province Yearbook, Bandar Anzali City, (2015).https://www.amar.org.ir
Peng, D. Zhang, Y. Liu(2005). “An improved snake model for building detection from urban aerial images”, Pattern Recognition Letters 26, pp: 587–595.http://scholar.google.com/scholar?q=An+improved+snake+model+for+building+detection+from+urban+aerial+images&hl=fa&as_sdt=0&as_vis=1&oi=scholart
Khani Pardanjani, Hamed; Shabanpur, Negar; Rezaei Farhadabad, Ali; Riahi Bakhtiari, Hamidreza. (2017). Extracting and reconstructing the three-dimensional building model using airborne lidar data and ultracam images, 25th National Geomatics Conference and Exhibition and 3rd Location Information Technology Engineering Conference, Tehran, pp. 7.https://civilica.com/doc/880017
WANG and H. LIU(2006). An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling, Department of Geography, Texas A&M University, College Station, TX 77843, USA, pp: 193-213.https://www.tandfonline.com/doi/abs/10.1080/13658810500433453
Gallay, C. Lloyd, J. Mckinley(2012). “Optimal interpolation of airborne laser scanning data for fine-scale dem validation purposes”. In Symposium GIS Ostrava, Proceedings Surface models for geosciences, Ostrava, Czech Republic. January, pp: 23-25.http://scholar.google.com/scholar?q=Optimal+interpolation+of+airborne+laser+scanning+data+for+fine-scale+dem+validation+purposes&hl=fa&as_sdt=0&as_vis=1&oi=scholart
Hebel(2008). “Pre-classification of points and segmentation of urban objects by scan line analysis of airborne lidar data”. The International Archives of Photogrammetry and Remote Sensing and Spatial Information Science XXXVII, pp: 1.http://nosazco.com/wp-content/uploads/2019/01/3d-building.pdf
Pan, X. Z. et al., (2008), Analyzing the Variation of Building Density Using High Spatial Resolution Satellite Images: the Example of Shanghai City, Sensors, Vol. 8, No. 4, pp: 2541-2550.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673430/
Parsiyan, Saeed. (2014). Building extraction using hyperspectral and lidar data integration, master's thesis, field of civil engineering and mapping, Tafresh University, pp. 1-2.https://gisj.sbu.ac.ir/index.php/GIS/article/view/article96491html
Rastiveis, H., Samadzadegan, F., & Reinartz, P(2013). A Fuzzy Decision Making System For Building Damage Map Creation Using High Resolution Satellite Imagery. Natural Hazards And Earth System Sciences, 13(2), pp: 455.http://scholar.google.com/scholar?q=A+Fuzzy+Decision+Making+System+For+Building+Damage+Map+Creation+Using+High+Resolution+Satellite+Imagery&hl=fa&as_sdt=0&as_vis=1&oi=scholart
Rottensteiner, F., Trinder, J., Clode, S., and Kubik, K.(2002), Detecting Buildings and Roof Segments by Combining LIDAR Data and Multispectral Images, Proceedings of Image and Vision Computing, Massey University, Palmerston North. N.Z., New Zealand. November,  pp: 6-65,26-28. https://www.researchgate.net/publication/37617535_Detecting_Buildings_and_Roof_Segments_by_Combining_LIDAR_Data_and_Multispectral_Images
Sajjadi, Seyed Yusuf; Parsian, Saeed. (2017). Building extraction using the integration of hyperspectral and lidar data using machine learning, remote sensing and GIS, Iran Research Institute of Humanities and Cultural Studies, 10th year, number 2, pp. 12.http://ensani.ir/fa/article/391748
Seif, Abdullah; Mahmoudi, Tayebe. (2014). Lidar sensor and its applications, Geographical Information Quarterly (Sepehr), Geographical Organization, Geographical Organization, Volume 22, No. 89, pp. 72-80.http://www.sepehr.org/article_13059.html
Shamsodini, Ali. (2016). Measuring the efficiency of lidar and optical data in order to extract the structural parameters of the forest, planning and planning of space, Research Institute of Humanities and Cultural Studies, Tarbiat Modares University, 21st term, number 2, pp. 119-145.http://ensani.ir/fa/article/285741/
Sivam, A. and Karuppannan, S. (2012) Density Design and Sustainable Residential Development, Presented at the European Network for Housing Research Conference 2009, 28 June to 1 July, Prague, Czech Republic, pp:2.http://scholar.google.com/scholar?q=Density+Design+and+Sustainable+Residential+Development,+Presented+at+the+European+Network+for+Housing+Research+Conference+2009&hl=fa&as_sdt=0&as_vis=1&oi=scholart
Sivam, A., Karuppannan, S. and Davis, M. C. (2011). Stakeholders' Perception of Residential Density: A Case Study of Adelaide, Journal of Housing and the Built Environment, pp: 2.http://scholar.google.com/scholar?q=).+Stakeholders%27+Perception+of+Residential+Density:+A+Case+Study+of+Adelaide&hl=fa&as_sdt=0&as_vis=1&oi=scholart
Elbernik, HG. Mass(2000). “The use of anisotropic height texture measurements for the segmentation of airborne laser scanner data”, pp: 33 .http://nosazco.com/wp-content/uploads/2019/01/3d-building.pdf
Forstner(1999). “3D-City Models: Automatic and Semiautomatic Acquisition Methods”. Gustafson EE, Kessel WC (1997) Fuzzy Clustering With A Fuzzy Covariance Matrix. San Diego, pp: 761–76 .http://nosazco.com/wp-content/uploads/2019/01/3d-building.pdf[
Wang, H., Sh,i S., and Rao X., (2013), A Study of Urban Density in Shenzhen, the Relationship between Street Morphology, Building Density and Land use, Proceedings of the Ninth International Space Syntax Symposium, Seoul, pp: 2.https://urs.ui.ac.ir/?_action=export&rf=isc&issue=4253
Wang, M., Tseng, Y(2004). LiDAR data segmentation and classification based on octree structure. International Archives of Photogrammetry and Remote Sensing 35, pp: 308-313.http://scholar.google.com/scholar?q=LiDAR+data+segmentation+and+classification+based+on+octree+structure.+International+Archives+of+Photogrammetry+and+Remote+Sensing&hl=fa&as_sdt=0&as_vis=1&oi=scholart
You, S. (2003). Urban Site Modeling from Lidar. 2nd Int’l Workshop Computer Graphics and Geometric Modeling (CGGM), Springer”, pp: 579 –588.https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.1368&rep=rep1&type=pdf
Geography and Territorial Spatial Arrangement
Volume 12, Issue 43
June 2022
Pages 1-30

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History

  • Receive Date: 29 May 2021
  • Revise Date: 30 November 2021
  • Accept Date: 12 December 2021

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