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Three-dimensional building change detection using object-based image analysis (case study: Tehran)

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Abstract

Natural disasters such as earthquakes and floods together with the urban sprawl conducted by increasing the population make multi-temporal changes in building areas. Destruction, buildings’ renovation, and constructing new buildings are the main changes of the urban areas that should be detected to update three-dimensional city models. The results of performing three-dimensional changes detecting of high altitude objects such as buildings are more close to reality than the two-dimensional methods. In this study, a three-dimensional changes detection method is proposed based on digital elevation models (DEMs). In the first step of this proposed method, the normalized digital surface model (nDSM) is generated for timely datasets. Then, object-based image analysis is utilized by performing segmentation followed by the structural classification of DEMs. Differencing and comparing the multi-temporal classification maps as the third step of the proposed algorithm led to analyzing the occurred changes. The obtained results are evaluated in an urban area in Tehran, Iran, in a 9-year time interval. These results represent −9.7% decreasing rate in low-rise buildings and also −1.37% decreases in the ground. Moreover, the class of high-rise buildings increased for +16.4% which conforms to making new constructions in addition to the renovation of low-rise buildings. According to the area analyzing of the changes, 4.8% of the investigated study area has new constructions, 3.05% has buildings’ renovation, and 3.89% has destruction in that 9-year period.

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References

  • Akca D, Freeman M, Sargent I, Gruen A (2010) Quality assessment of 3D building data. Photogramm Rec 25(132):339–355

    Article  Google Scholar 

  • Bouziani M, Goïta K, He D-C (2010) Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS J Photogramm Remote Sens 65(1):143–153

    Article  Google Scholar 

  • Brunner D, Lemoine G, Bruzzone L (2010) Earthquake damage assessment of buildings using VHR optical and SAR imagery. Geoscience and Remote Sensing, IEEE Transactions on 48(5):2403–2420

    Article  Google Scholar 

  • Chaabouni-Chouayakh H, Reinartz P (2011) Towards automatic 3D change detection inside urban areas by combining height and shape information. Photogrammetrie-Fernerkundung-Geoinformation 2011(4):205–217

    Article  Google Scholar 

  • Chaabouni-Chouayakh H, Krauss T, d’Angelo P, Reinartz P (2010) 3D change detection inside urban areas using different digital surface models. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38:86–91

    Google Scholar 

  • Chaabouni-Chouayakh H, d'Angelo P, Krauss T, Reinartz P (2011) Automatic urban area monitoring using digital surface models and shape features. In: Urban Remote Sensing Event (JURSE), 2011 Joint, pp 85–88

    Chapter  Google Scholar 

  • Champion N, Boldo D, Pierrot-Deseilligny M, Stamon G (2010) 2D building change detection from high resolution satellite imagery: a two-step hierarchical method based on 3D invariant primitives. Pattern Recogn Lett 31(10):1138–1147

    Article  Google Scholar 

  • Daniel S, Doran MA (2013) geoSmartCity: geomatics contribution to the smart city. In: Proceedings of the 14th Annual International Conference on Digital Government Research, pp 65–71

    Chapter  Google Scholar 

  • Dini G, Jacobsen K, Rottensteiner F, Al Rajhi M, Heipke C (2012) 3D building change detection using high resolution stereo images and a GIS database. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1:299–304

    Article  Google Scholar 

  • Eden I, Cooper DB (2008) Using 3D line segments for robust and efficient change detection from multiple noisy images. In: 10th European Conference on Computer Vision, Marseille, France, 12–18, October, pp 172–185

    Google Scholar 

  • Ghorbani M, Sarkargar Ardakani A, Ayazi M (2014) 3D change detection of buildings using high resolution images and three dimensional database. Geomatics conference (21)

  • Gong P, Biging GS, Standiford R (2000) Technical note: use of digital surface model for hardwood rangeland monitoring. J Range Manag 53(6):622–626

    Article  Google Scholar 

  • Gruen A (2013) Next generation smart cities-the role of geomatics. BBC 26.17: 32.81 G 547(25):25

    Google Scholar 

  • Gruen A, Akca D (2005) Least squares 3D surface and curve matching. ISPRS J Photogramm Remote Sens 59(3):151–174

    Article  Google Scholar 

  • Guerin C, Binet R, Pierrot-Deseilligny M (2014) Automatic detection of elevation changes by differential DSM analysis: application to urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(10):4020–4037

    Article  Google Scholar 

  • Heller AJ, Leclerc YG, Luong Q-T (2001) Framework for robust 3D change detection. In: International Symposium on Remote Sensing, pp 639–649

    Google Scholar 

  • Huang X, Zhang L, Zhu T (2014) Building change detection from multi temporal high-resolution remotely sensed images based on a morphological building index. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 7(1):105–115

    Article  Google Scholar 

  • Jung F (2004) Detecting building changes from multitemporal aerial stereopairs. ISPRS J Photogramm Remote Sens 58(3):187–201

    Article  Google Scholar 

  • Martha TR, Kerle N, Jetten V, Westen CJ, Kumar KV (2010) Landslide volumetric analysis using Cartosat-1-derived DEMs. IEEE Geosci Remote Sens Lett 7(3):582–586

    Article  Google Scholar 

  • Matikainen L, Hyyppä J, Ahokas E, Markelin L, Kaartinen H (2010) Automatic detection of buildings and changes in buildings for updating of maps. Remote Sens 2(5):1217–1248

    Article  Google Scholar 

  • Nebiker S, Lack N, Deuber M (2014) Building change detection from historical aerial photographs using dense image matching and object-based image analysis. Remote Sens 6(9):8310–8336

    Article  Google Scholar 

  • Pang S, Hu X, Wang Z, Lu Y (2014) Object-based analysis of airborne LiDAR data for building change detection. Remote Sens 6(11):10733–10749

    Article  Google Scholar 

  • Qin R, Gruen A (2014) 3D change detection at street level using mobile laser scanning point clouds and terrestrial images. ISPRS J Photogramm Remote Sens 90(2014):23–35

    Article  Google Scholar 

  • Qin R, Tian J, Reinartz P (2016) 3D change detection – Approaches and applications. ISPRS Journal of Photogrammetry and Remote Sensing (122):41–56. https://doi.org/10.1016/j.isprsjprs.2016.09.013

  • Rottensteiner F, Trinder J, Clode S, Kubik K (2007) Building detection by fusion of airborne laser scanner data and multi-spectral images: performance evaluation and sensitivity analysis. ISPRS J Photogramm Remote Sens 62(2):135–149

    Article  Google Scholar 

  • Sasagawa A, Watanabe K, Nakajima S, Koido K, Ohno H, Fujimura H (2008) Automatic change detection based on pixel-change and DSM-change. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37(Part B7):1645–1650

    Google Scholar 

  • Sasagawa A, Baltsavias E, Aksakal SK, Wegner JD (2013) Investigation on automatic change detection using pixel-changes and DSM-changes with ALOS-PRISM triplet images. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1(2):213–217

    Article  Google Scholar 

  • Schenk T, Krupnik A, Postolov Y (2000) Comparative study of surface matching algorithms. International Archives of Photogrammetry and Remote Sensing 33(B4):518–524

    Google Scholar 

  • Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003

    Article  Google Scholar 

  • Stal C, Tack F, De Maeyer P, De Wulf A, Goossens R (2013) Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area–a comparative study. Int J Remote Sens 34(4):1087–1110

    Article  Google Scholar 

  • Tabib Mahmoudi F, Samadzadegan F, Reinartz P (2013) Object oriented image analysis based on multi-agent recognition system. Comput Geosci 54(2013):219–230

    Article  Google Scholar 

  • Tian J, Chaabouni-Chouayakh H, Reinartz P, Krauß T, d'Angelo P (2010) Automatic 3D change detection based on optical satellite stereo imagery. Int Arch Photogram Remote Sens Spatial Inf Sci 38(Part 7B):586–591

  • Tian J, Nielsen AA, Reinartz P (2014) Improving change detection in forest areas based on stereo panchromatic imagery using kernel MNF. IEEE Trans Geosci Remote Sens 52(11):7130–7139

    Article  Google Scholar 

  • Vakalopoulou M, Karantzalos K, Komodakis N, Paragios N (2015) Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 61–69

    Google Scholar 

  • Vu T, Matsuoka M, Yamazaki F (2004) LIDAR-based change detection of buildings in dense urban areas. In: Geoscience and Remote Sensing Symposium. IGARSS'04. IEEE International, pp 3413–3416

  • Waser L, Baltsavias E, Ecker K, Eisenbeiss H, Feldmeyer-Christe E, Ginzler C, Küchler M, Zhang L (2008) Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images. Remote Sens Environ 112(5):1956–1968

    Article  Google Scholar 

  • Xiao W, Vallet B, Paparoditis N (2013) Change detection in 3D point clouds acquired by a mobile mapping system. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 1(2):331–336

    Article  Google Scholar 

  • Zavodny AG (2012) Change detection in LiDAR scans of urban environments. University of Notre Dame, Computer Science and Engineering

    Google Scholar 

Download references

Funding

This work was supported by Shahid Rajaee Teacher Training University under contract number 17538.

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Correspondence to Fatemeh Tabib Mahmoudi.

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Tabib Mahmoudi, F., Hosseini, S. Three-dimensional building change detection using object-based image analysis (case study: Tehran). Appl Geomat 13, 325–332 (2021). https://doi.org/10.1007/s12518-020-00349-w

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  • DOI: https://doi.org/10.1007/s12518-020-00349-w

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