Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

High-quality building information models (BIMs) using geospatial datasets

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Recently, there has been a great demand for 3D building models in several applications including cartography and planning applications in urban areas. This led to the development of automated algorithms to extract such models since they reduce the time and cost when compared to manually onscreen digitizing. Most algorithms are built to solve the proposed problem from either LiDAR datasets or aerial imageries. Since both datasets have their weaknesses, integrating these datasets has the potential to be more successful in 3D modeling since the limitations of each source can be fulfilled by the other. In this article, we outline an algorithm that generates 3D building wireframes from LiDAR DEMs and high-resolution aerial images. Each post in the DEM is assigned five different attributes representing the intensity and elevations in its neighborhood. Posts are then classified as ground or non-ground using a feedforward back-propagation neural network. Non-ground points are grouped into different planer patches using Hough transformation, and these patches are iteratively refined using the L1-norm blunder detector and a region-growing segmentation algorithm. Finally, topological relationships among roof planes and boundary points are satisfied through regression analyses. The algorithm is tested on a number of buildings with complex rooftops, and results show its promising precision and completeness in modeling various building shapes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  • Awrangjeb M, Fraser CS (2014) Automatic segmentation of raw LiDAR data for extraction of building roofs. Remote Sens 6(5):3716–3751

    Article  Google Scholar 

  • Borovicka T, Jirina M, Kordik P, Jirina M (2012) Selecting representative data sets. Advances in data mining knowledge discovery and applications, 43–70

  • Borrmann D, Elseberg J, Lingemann K, Nüchter A (2011) The 3D Hough transform for plane detection in point clouds: a review and a new accumulator design. 3D Res 2(2):1–13

    Article  Google Scholar 

  • Cheng L, Tong L, Chen Y, Zhang W, Shan J, Liu Y, Li M (2013) Integration of LiDAR data and optical multi-view images for 3D reconstruction of building roofs. Opt Lasers Eng 51(4):493–502

    Article  Google Scholar 

  • Demir N, Baltsavias E (2012) Automated modeling of 3D building roofs using image and LiDAR data. In: Proceedings of the XXII Congress of the International Society for Photogrammetry, Remote Sensing, Melbourne, Australia, vol. 25

  • Elaksher A, Bethel J (2008) Automatic generation of high-quality three-dimensional urban buildings from aerial images. URISA J 20(1):5–13

    Google Scholar 

  • Elaksher A, Bethel J (2010) Refinement of digital elevation models in urban areas using breaklines via a multi-photo least squares matching algorithm. J Terr Observ 2(2):67–80

    Google Scholar 

  • Elkan C (2012) Evaluating classifiers. University of San Diego, California, retrieved [01-12-2016] from http://cseweb.ucsd.edu/~elkan/250B/. Accecced 23 Feb 2021

  • Foody GM (1995) Land cover classification by an artificial neural network with ancillary information. Int J Geogr Inf Syst 9(5):527–542

    Article  Google Scholar 

  • Gellert W, Gottwald S, Hellwich M, Kästner H, Künstner H (1989) VNR Concise encyclopedia of mathematics, 2nd edn. Van Nostrand Reinhold, New York, pp 541–543

    Google Scholar 

  • Gerke M, Xiao J (2014) Fusion of airborne laser scanning point clouds and images for supervised and unsupervised scene classification. ISPRS J Photogramm Remote Sens 87:78–92

    Article  Google Scholar 

  • Gilani SAN, Awrangjeb M, Lu G (2015) Fusion of LiDAR data and multispectral imagery for effective building detection based on graph and connected component analysis. Int Arch Photogramm Remote Sens Spat Inf Sci XL(3/W2):65–72

    Article  Google Scholar 

  • Gilani S, Naqi A, Awrangjeb M, Lu G (2016) An automatic building extraction and regularisation technique using LiDAR point cloud data and orthoimage. Remote Sens 8(3):258

    Article  Google Scholar 

  • Gonçalves G (2006) Analysis of interpolation errors in urban digital surface models created from LiDAR data. In: Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Resources and Environment Sciences 5–7 July, Lisbon, Portugal. https://www.spatial-accuracy.org/system/files/Goncalves2006accuracy.pdf. Accecced 23 Feb 2021

  • Haala N, Rothermel M, Cavegn S (2015) Extracting 3D urban models from oblique aerial images. In: Urban Remote Sensing Event (JURSE), 2015 Joint, pp. 1–4. IEEE

  • Habib AF, Zhai R, Kim C (2010) Generation of complex polyhedral building models by integrating stereo-aerial imagery and LiDAR data. Photogramm Eng Remote Sens 76(5):609–623

    Article  Google Scholar 

  • Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan Publishing, New York

    Google Scholar 

  • Hepner GF (1990) Artificial neural network classification using a minimal training set. Comparison to conventional supervised classification. Photogramm Eng Remote Sens 56(4):469–473

    Google Scholar 

  • Kim Y, Kim Y (2014) Improved classification accuracy based on the output-level fusion of high-resolution satellite images and airborne LiDAR data in urban area. Geosci Remote Sens Lett IEEE 11(3):636–640

    Article  Google Scholar 

  • Kwak E, Habib A (2014) Automatic representation and reconstruction of DBM from LiDAR data using recursive minimum bounding rectangle. ISPRS J Photogramm Remote Sens 93:171–191

    Article  Google Scholar 

  • Kwak E, Al-Durgham M, Habib A (2012) Automatic 3D building model generation from lidar and image data using sequential minimum bounding rectangle. Int Arch Photogramm Remote Sens Spat Inf Sci XXXIX(B3):285–290

    Article  Google Scholar 

  • Li Y, Wu H, An R, Xu H, He Q, Xu J (2013) An improved building boundary extraction algorithm based on fusion of optical imagery and LiDAR data. Optik Int J Light Electron Opt 124(22):5357–5362

    Article  Google Scholar 

  • Li Y, Zhu L, Tachibana K, Shimamura H, Li M (2016) Morphological house extraction from digital surface model and imagery of high-density residential areas. Photogramm Eng Remote Sens 82(1):21–29

    Article  Google Scholar 

  • Marshall J, Bethel J (1996) Basic concepts of L1 norm minimization for surveying applications. J Surv Eng 122(4):168–179

    Article  Google Scholar 

  • Mikhail EM (1976) Observation and least squares. NY University Press, New York

    Google Scholar 

  • Mikhail EM, Bethel JS, McGlone JC (2001) Introduction to modern photogrammetry, vol 1. Wiley, New York

    Google Scholar 

  • 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 93:145–156

    Article  Google Scholar 

  • Shan J, Sampath A (2005) Urban DEM generation from raw LiDAR data: a labeling algorithm and its performance. Photogramm Eng Remote Sens 71(2):217–226

    Article  Google Scholar 

  • Sun J (2012) Learning algorithm and hidden node selection scheme for local coupled feedforward neural network classifier. Neurocomputing 79:158–163

    Article  Google Scholar 

  • Tarantino E, Figorito B (2011) Extracting buildings from true color stereo aerial images using a decision making strategy. Remote Sens 3(8):1553–1567

    Article  Google Scholar 

  • Uzar M (2014) Automatic building extraction with multi-sensor data using rule-based classification. Eur J Remote Sens 47(8):1–18

    Article  Google Scholar 

  • Wang R (2013) 3D building modeling using images and LiDAR: a review. Int J Image Data Fusion 4(4):273–292

    Article  Google Scholar 

  • Xiao J, Gerke M, Vosselman G (2012) Building extraction from oblique airborne imagery based on robust façade detection. ISPRS J Photogramm Remote Sens 68:56–68

    Article  Google Scholar 

  • Yang B, Xu W, Yao W (2014) Extracting buildings from airborne laser scanning point clouds using a marked point process. GISci Remote Sens 51(5):555–574

    Article  Google Scholar 

  • Zarea A, Mohammadzadeh A (2016) A novel building and tree detection method from lidar data and aerial images. IEEE J Sel Top Appl Earth Observ Remote Sens 9(5):1864–1875

    Article  Google Scholar 

  • Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 30(4):451–462

    Article  Google Scholar 

  • Zhang K, Chen S, Whitman D, Shyu M, Yan J, Zhang C (2003) A progressive morphological filter for removing nonground measurements from airborne LiDAR data. IEEE Trans Geosci Remote Sens 41(4):872–882

    Article  Google Scholar 

  • Zhang W, Wang H, Chen Y, Yan K, Chen M (2014) 3D building roof modeling by optimizing primitive’s parameters using constraints from LiDAR data and aerial imagery. Remote Sens 6(9):8107–8133

    Article  Google Scholar 

  • Zhou G, Zhou X (2014) Seamless fusion of LiDAR and aerial imagery for building extraction. IEEE Transactions on Geoscience and Remote Sensing 52(11):7393–7407

Download references

Author information

Authors and Affiliations

  1. College of Engineering, New Mexico State University, Las Cruces, NM, 88003, USA

    Elaksher Ahmed

  2. College of Engineering, American University of Sharjah, Sharjah, United Arab Emirates

    Ali Tarig

  3. School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Bethel James

Authors
  1. Elaksher Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Tarig
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bethel James
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elaksher Ahmed.

Additional information

Communicated by: H. Babaie

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmed, E., Tarig, A. & James, B. High-quality building information models (BIMs) using geospatial datasets. Earth Sci Inform 14, 847–860 (2021). https://doi.org/10.1007/s12145-021-00591-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-021-00591-9

Keywords

Search

Navigation