Handling Massive Data Size Issue in Buildings Footprints Extraction from High-Resolution Satellite Images

  • Sohaib K. M. AbujayyabEmail author
  • Ismail Rakip KarasEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1188)


Building information modelling BIM is relying on plenty of geospatial information such as buildings footprints. Collecting and updating BIM information is a considerable challenge. Recently, buildings footprints automatically extracted from high-resolution satellite images utilizing machine learning algorithms. Constructing required training datasets for machine learning algorithms and testing data is computationally intensive. When the analysis performs in large geographic areas, researchers are struggling from out of memory problems. The requirement of developing improved, fit memory computation methods for accomplishing this computation is urgent. This paper targeting to handling massive data size issue in buildings footprints extraction from high-resolution satellite images. This article established a method to process the spatial raster data based on the chunks computing. Chunk-based decomposition decomposes raster array into several tiny cubes. Cubes supposed to be small enough to fit into available memory and prevent memory overflow. The algorithm of the method developed using Python programming language. Spatial data and developed tool were prepared and processed in ArcGIS software. Matlab software utilized for machine learning. Neural networks implemented for extracting the buildings’ footprints. To demonstrate the performance of our approach, high-resolution Orthoimage located in Tucson, Arizona state in American United States was utilized as a case study. Original image was taken by UltraCamEagle sensor and contained (11888 columns, 11866 rows, cell size 0.5 foot, 564,252,032 pixels in 4 bands). The case image contained (1409 columns, 1346 rows, and 7586056 pixels in 4 bands). The full image is impossible to be handled in the traditional central processing unit CPU. The image divided to 36 chunks using 1000 rows and 1000 columns. Full analysis spent 35 min using Intel Core i7 processor. The output performance accuracy of the neural network is 98.3% for testing dataset. Consequences demonstrate that the chunk computing can solve the memory overflow in personal computers during buildings footprints extraction process, especially in case of processing large files of high-resolution images. The developed method is suitable to be implemented in an affordable lightweight desktop environment. In addition, building footprints extracted effetely and memory overflow problem bypassed. Furthermore, the developed method proved the high quality extracted buildings footprints that can be integrated with BIM applications.


Buildings Information Modelling Buildings footprints extraction Massive data size High resolution satellite images Neural networks 



This study has been supported by 2221 – Fellowship Program of TUBITAK (The Scientific and Technological Research Council of Turkey). We are indebted for their supports.


  1. 1.
    Kreiner, H., Passer, A., Wallbaum, H.: A new systemic approach to improve the sustainability performance of office buildings in the early design stage. Energy Build. 109, 385–396 (2015). Scholar
  2. 2.
    Cavalliere, C., Dell’Osso, G.R., Pierucci, A., Iannone, F.: Life cycle assessment data structure for building information modelling. J. Clean. Prod. 199, 193–204 (2018). Scholar
  3. 3.
    Muller, M.F., Esmanioto, F., Huber, N., Loures, E.R., Canciglieri, O.: A systematic literature review of interoperability in the green Building Information Modeling lifecycle. J. Clean. Prod. 223, 397–412 (2019). Scholar
  4. 4.
    Yin, X., Liu, H., Chen, Y., Al-Hussein, M.: Building information modelling for off-site construction: review and future directions. Autom. Constr. 101, 72–91 (2019). Scholar
  5. 5.
    Wikipedia: Building information modeling. Wikipedia (2019). (accessed May 13, 2019)
  6. 6.
    Japanese Earth observing satellite, Advanced Land Observing Satellite - Phased Array type L-band Synthetic Aperture Radar (2019). Accessed 9 Apr 2019
  7. 7.
    Alaska Satellite Facility’s: ALOS Dataset Information (2011). Accessed 11 Apr 2019
  8. 8.
    Nefeslioglu, H.A., San, B.T., Gokceoglu, C., Duman, T.Y.: An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping. Int. J. Appl. Earth Obs. Geoinf. 14, 40–60 (2012). Scholar
  9. 9.
    Park, Y., Guldmann, J.-M.: Creating 3D city models with building footprints and LIDAR point cloud classification: a machine learning approach. Comput. Environ. Urban Syst. 75, 76–89 (2019). Scholar
  10. 10.
    Sinha, R., Lennartsson, M., Frostell, B.: Environmental footprint assessment of building structures: a comparative study. Build. Environ. 104, 162–171 (2016). Scholar
  11. 11.
    Green Build: Building footprint, Green, Build (2019). Accessed 13 May 2019
  12. 12.
    Tournaire, O., Brédif, M., Boldo, D., Durupt, M.: An efficient stochastic approach for building footprint extraction from digital elevation models. ISPRS J. Photogramm. Remote Sens. 65, 317–327 (2010). Scholar
  13. 13.
    Huang, J., Zhang, X., Xin, Q., Sun, Y., Zhang, P.: Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network. ISPRS J. Photogramm. Remote Sens. 151, 91–105 (2019). Scholar
  14. 14.
    Gavankar, N.L., Ghosh, S.K.: Automatic building footprint extraction from high-resolution satellite image using mathematical morphology. Eur. J. Remote Sens. 51, 182–193 (2018). Scholar
  15. 15.
    Hamzeh, M., Abbaspour, R.A., Davalou, R.: Raster-based outranking method: a new approach for municipal solid waste landfill (MSW) siting. Environ. Sci. Pollut. Res. 22, 12511–12524 (2015). Scholar
  16. 16.
    Li, D., Wang, S., Li, D.: Spat. Data Min. (2015). Scholar
  17. 17.
    Li, J., Finn, M.P., Blanco Castano, M.: A lightweight CUDA-based parallel map reprojection method for raster datasets of continental to global extent. ISPRS Int. J. Geo-Inf. 6, 92 (2017). Scholar
  18. 18.
    Norman, M., Mohd Shafri, H.Z., Idrees, M.O., Mansor, S., Yusuf, B.: Spatio-statistical optimization of image segmentation process for building footprint extraction using very high-resolution WorldView 3 satellite data. Geocarto Int. 1–24 (2019).
  19. 19.
    Gruber, M., Ponticelli, M., Ladstädter, R., Wiechert, A.: UltraCam Eagle, details and insight. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 39 (2012)Google Scholar
  20. 20.
    Al-Mahallawi, K., Mania, J., Hani, A., Shahrour, I.: Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas. Environ. Earth Sci. 65, 917–928 (2012)CrossRefGoogle Scholar
  21. 21.
    Vallet, B., Pierrot-Deseilligny, M., Boldo, D., Brédif, M.: Building footprint database improvement for 3D reconstruction: a split and merge approach and its evaluation. ISPRS J. Photogramm. Remote Sens. 66, 732–742 (2011). Scholar
  22. 22.
    Brédif, M., Tournaire, O., Vallet, B., Champion, N.: Extracting polygonal building footprints from digital surface models: a fully-automatic global optimization framework. ISPRS J. Photogramm. Remote Sens. 77, 57–65 (2013). Scholar
  23. 23.
    Alshehhi, R., Marpu, P.R., Woon, W.L., Mura, M.D.: Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J. Photogramm. Remote Sens. 130, 139–149 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of GeographyKarabuk UniversityKarabukTurkey
  2. 2.Department of Computer EngineeringKarabuk UniversityKarabukTurkey

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