Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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). https://doi.org/10.1016/j.enbuild.2015.09.040
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). https://doi.org/10.1016/j.jclepro.2018.07.149
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). https://doi.org/10.1016/j.jclepro.2019.03.114
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). https://doi.org/10.1016/j.autcon.2019.01.010
Wikipedia: Building information modeling. Wikipedia (2019). https://en.wikipedia.org/wiki/Building_information_modeling (accessed May 13, 2019)
Japanese Earth observing satellite, Advanced Land Observing Satellite - Phased Array type L-band Synthetic Aperture Radar (2019). https://www.eorc.jaxa.jp/ALOS/en/about/about_index.htm. Accessed 9 Apr 2019
Alaska Satellite Facility’s: ALOS Dataset Information (2011). https://vertex.daac.asf.alaska.edu/. Accessed 11 Apr 2019
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). https://doi.org/10.1016/j.jag.2011.08.005
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). https://doi.org/10.1016/j.compenvurbsys.2019.01.004
Sinha, R., Lennartsson, M., Frostell, B.: Environmental footprint assessment of building structures: a comparative study. Build. Environ. 104, 162–171 (2016). https://doi.org/10.1016/j.buildenv.2016.05.012
Green Build: Building footprint, Green, Build (2019). https://leeduser.buildinggreen.com/forum/building-footprint-6. Accessed 13 May 2019
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). https://doi.org/10.1016/j.isprsjprs.2010.02.002
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). https://doi.org/10.1016/j.isprsjprs.2019.02.019
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). https://doi.org/10.1080/22797254.2017.1416676
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). https://doi.org/10.1007/s11356-015-4485-8
Li, D., Wang, S., Li, D.: Spat. Data Min. (2015). https://doi.org/10.1007/978-3-662-48538-5
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). https://doi.org/10.3390/ijgi6040092
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). https://doi.org/10.1080/10106049.2019.1573853
Gruber, M., Ponticelli, M., Ladstädter, R., Wiechert, A.: UltraCam Eagle, details and insight. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 39 (2012)
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)
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). https://doi.org/10.1016/j.isprsjprs.2011.06.005
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). https://doi.org/10.1016/j.isprsjprs.2012.11.007
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). https://doi.org/10.1016/j.isprsjprs.2017.05.002
Acknowledgment
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.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Abujayyab, S.K.M., Karas, I.R. (2020). Handling Massive Data Size Issue in Buildings Footprints Extraction from High-Resolution Satellite Images. In: Ofluoglu, S., Ozener, O., Isikdag, U. (eds) Advances in Building Information Modeling. EBF 2019. Communications in Computer and Information Science, vol 1188. Springer, Cham. https://doi.org/10.1007/978-3-030-42852-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-42852-5_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-42851-8
Online ISBN: 978-3-030-42852-5
eBook Packages: Computer ScienceComputer Science (R0)