Change detection in urban landscapes: a tensor factorization approach

  • S. SarithaEmail author
  • G. Santhosh Kumar


Analysis of urban landscape has been an interesting research challenge for decades. The advent of machine learning and data mining techniques have geared the problem from simple analysis of data to knowledge discovery from data. This work attempts to mine urban landscapes to find the change pattern which has happened over the region for a period of interest. The work proposes a spatiotemporal-metric miner, which uses the spatial, temporal and landscape metric data to discover the change that has occurred in a region. The model works on a hierarchical basis, wherein, the regions of interest are chosen in a landscape and are aggregated to find the change that has happened over the entire region. The entire model is built by taking advantage of the tensorized representation of data, and thus resulting in the effective mining of tensors. The growth of a landscape is evaluated regarding two parameters, namely, Inter-class Growth Index and Intra-class Growth Index. Experiments are performed on the landscape regions of Indian cities, and a ranking of cities is presented based on the growth indices, which are validated against standards. In the experiments, Jaipur city showed the highest Inter-class Growth Index value of 2.68 and Surat city had an Intra-class Growth Index of 0.78.


Data mining Spatiotemporal mining Change detection Remote sensing 


Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. 1.
    Acker, J. G., & Leptoukh, G. (2007). Online analysis enhances use of NASA earth science data. Eos, Transactions American Geophysical Union, 88(2), 14–17.CrossRefGoogle Scholar
  2. 2.
    Hwang, D. H., Kim, H. M., Bak, S. H., Oh, S. Y., Yoon, H. J., & Chung, Y. H. (2016). Study of the temporal and spatial analysis by using SST satellite data. International Journal of Multimedia and Ubiquitous Engineering, 11(12), 149–160.CrossRefGoogle Scholar
  3. 3.
    Liu, X., Ma, L., Li, X., Ai, B., Li, S., & He, Z. (2014). Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. International Journal of Geographical Information Science, 28(1), 148–163.CrossRefGoogle Scholar
  4. 4.
    Park, S., Jeon, S., Kim, S., & Choi, C. (2011). Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landscape and Urban Planning, 99(2), 104–114.CrossRefGoogle Scholar
  5. 5.
    Vakalopoulou, M., Karantzalos, K., Komodakis, N., & Paragios, N. (2015). Building detection in very high resolution multispectral data with deep learning features. In IEEE international geoscience and remote sensing symposium (IGARSS), 2015 (pp. 1873–1876). IEEE.Google Scholar
  6. 6.
    Albert, A., Kaur, J., & Gonzalez, M. C. (2017). Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1357–1366). ACM.Google Scholar
  7. 7.
    Hu, T., Yang, J., Li, X., & Gong, P. (2016). Mapping urban land use by using landsat images and open social data. Remote Sensing, 8(2), 151.CrossRefGoogle Scholar
  8. 8.
    Lang, S., Walz, U., Klug, H., Blaschke, T., & Syrbe, R. U. (2008). Landscape Metrics-A toolbox for assessing past, present and future landscape structures. Geoinformation Technologies for Geocultural Landscapes: European Perspectives, 207, 207.CrossRefGoogle Scholar
  9. 9.
    Tewkesbury, A. P., Comber, A. J., Tate, N. J., Lamb, A., & Fisher, P. F. (2015). A critical synthesis of remotely sensed optical image change detection techniques. Remote Sensing of Environment, 160, 1–14.CrossRefGoogle Scholar
  10. 10.
    Alphan, H., Doygun, H., & Unlukaplan, Y. I. (2009). Post-classification comparison of land cover using multitemporal Landsat and ASTER imagery: The case of Kahramanmaraş. Turkey. Environmental Monitoring and Assessment, 151(1–4), 327–336.CrossRefGoogle Scholar
  11. 11.
    Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2013). Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, 122–131.CrossRefGoogle Scholar
  12. 12.
    Comber, A., Balzter, H., Cole, B., Fisher, P., Johnson, S., & Ogutu, B. (2016). Methods to quantify regional differences in land cover change. Remote Sensing, 8(3), 176.CrossRefGoogle Scholar
  13. 13.
    Miller, O., Pikaz, A., & Averbuch, A. (2005). Objects based change detection in a pair of gray-level images. Pattern Recognition, 38(11), 1976–1992.CrossRefGoogle Scholar
  14. 14.
    Lefebvre, A., Corpetti, T., & Hubert-Moy, L. (2008). Object-oriented approach and texture analysis for change detection in very high resolution images. In IEEE international geoscience and remote sensing symposium, 2008. IGARSS 2008 (Vol. 4, pp. IV-663). IEEE.Google Scholar
  15. 15.
    Desclée, B., Bogaert, P., & Defourny, P. (2006). Forest change detection by statistical object-based method. Remote Sensing of Environment, 102(1–2), 1–11.CrossRefGoogle Scholar
  16. 16.
    Bontemps, S., Bogaert, P., Titeux, N., & Defourny, P. (2008). An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sensing of Environment, 112(6), 3181–3191.CrossRefGoogle Scholar
  17. 17.
    Conchedda, G., Durieux, L., & Mayaux, P. (2008). An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 63(5), 578–589.CrossRefGoogle Scholar
  18. 18.
    Li, X., Yeh, A. G. O., Qian, J. P., Ai, B., & Qi, Z. (2009). A matching algorithm for detecting land use changes using case-based reasoning. Photogrammetric Engineering & Remote Sensing, 75(11), 1319–1332.CrossRefGoogle Scholar
  19. 19.
    Réjichi, S., Chaabane, F., & Tupin, F. (2015). Expert knowledge-based method for satellite image time series analysis and interpretation. IEEE JSTARS, 8(5), 2138–2150.Google Scholar
  20. 20.
    Benito-Calvo, A., Pérez-González, A., Magri, O., & Meza, P. (2009). Assessing regional geodiversity: The Iberian Peninsula. Earth Surface Processes and Landforms, 34(10), 1433–1445.CrossRefGoogle Scholar
  21. 21.
    Alhamad, M. N., Alrababah, M. A., Feagin, R. A., & Gharaibeh, A. (2011). Mediterranean drylands: The effect of grain size and domain of scale on landscape metrics. Ecological Indicators, 11(2), 611–621.CrossRefGoogle Scholar
  22. 22.
    Gallardo, B., Gascón, S., Quintana, X., & Comín, F. A. (2011). How to choose a biodiversity indicator–redundancy and complementarity of biodiversity metrics in a freshwater ecosystem. Ecological Indicators, 11(5), 1177–1184.CrossRefGoogle Scholar
  23. 23.
    Uuemaa, E., Antrop, M., Roosaare, J., Marja, R., & Mander, Ü. (2009). Landscape metrics and indices: An overview of their use in landscape research. Living Reviews in Landscape Research, 3(1), 1–28.Google Scholar
  24. 24.
    Fan, C., & Myint, S. (2014). A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation. Landscape and Urban Planning, 121, 117–128.CrossRefGoogle Scholar
  25. 25.
    Liu, M., Zhang, Z. M., Yang, M. Y., Geng, Y. P., Ou, X. K., & Song, D. (2016). Analysis of urban public greenspace pattern based on landscape metrics in Kunming. In Civil engineering and urban planning IV: Proceedings of the 4th international conference on civil engineering and urban planning, Beijing, China, 2527 July 2015(p. 127). CRC Press.Google Scholar
  26. 26.
    Peng, Y., Mi, K., Qing, F., & Xue, D. (2016). Identification of the main factors determining landscape metrics in semi-arid agro-pastoral ecotone. Journal of Arid Environments, 124, 249–256.CrossRefGoogle Scholar
  27. 27.
    Chen, B. H., Teng, S. Y., & Chuang, K. T. (2017). Mining spatio-temporal chaining patterns in non-identity event databases. Intelligent Data Analysis, 21(S1), S71–S102.CrossRefGoogle Scholar
  28. 28.
    Alatrista-Salas, H., Bringay, S., Flouvat, F., Selmaoui-Folcher, N., & Teisseire, M. (2016). Spatio-sequential patterns mining: Beyond the boundaries. Intelligent Data Analysis, 20(2), 293–316.CrossRefGoogle Scholar
  29. 29.
    Saha, S., Murthy, C. A., & Pal, S. K. (2009). Tensor framework and combined symmetry for hypertext mining. Fundamenta Informaticae, 97(1–2), 215–234.Google Scholar
  30. 30.
    Gauvin, L., Panisson, A., & Cattuto, C. (2014). Detecting the community structure and activity patterns of temporal networks: A non-negative tensor factorization approach. PLoS ONE, 9(1), e86028.CrossRefGoogle Scholar
  31. 31.
    Zhao, X., Deng, N., & Jing, L. (2017). Application of image recognition in civil aviation security based on tensor learning. Journal of Intelligent & Fuzzy Systems, 33(4), 2145–2157.CrossRefGoogle Scholar
  32. 32.
    Tan, H., Feng, G., Feng, J., Wang, W., Zhang, Y. J., & Li, F. (2013). A tensor-based method for missing traffic data completion. Transportation Research Part C: Emerging Technologies, 28, 15–27.CrossRefGoogle Scholar
  33. 33.
    Chen, X., He, Z., & Sun, L. (2019). A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 98, 73–84.CrossRefGoogle Scholar
  34. 34.
    Wang, Y., Zheng, Y., & Xue, Y. (2014). Travel time estimation of a path using sparse trajectories. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 25–34). ACM.Google Scholar
  35. 35.
    Lykov, S., & Asakura, Y. (2018). Anomalous traffic pattern detection in large urban areas: Tensor-based approach with continuum modeling of traffic flow. International Journal of Intelligent Transportation Systems Research. Scholar
  36. 36.
    Liao, J., Tang, J., Zhao, X., & Shang, H. (2018). Improving POI recommendation via dynamic tensor completion. Scientific Programming. Scholar
  37. 37.
    Zhou, L., Du, G., Wang, R., Tao, D., Wang, L., Cheng, J., et al. (2019). A tensor framework for geosensor data forecasting of significant societal events. Pattern Recognition, 88, 27–37.CrossRefGoogle Scholar
  38. 38.
    McGarigal, K., Cushman, S. A., Neel, M. C., & Ene, E. (2002). FRAGSTATS: Spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site:
  39. 39.
    Saritha, S., & Kumar, G. S. (2017). Analysis of the smart growth of kochi city through landscape metrics. In IEEE region 10 symposium (TENSYMP), 2017 (pp. 1–5). IEEE.Google Scholar
  40. 40.
    Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM Review, 51(3), 455–500.CrossRefGoogle Scholar
  41. 41.
    Bro, R., & Kiers, H. A. (2003). A new efficient method for determining the number of components in PARAFAC models. Journal of Chemometrics: A Journal of the Chemometrics Society, 17(5), 274–286.CrossRefGoogle Scholar
  42. 42.
    De Lathauwer, L., De Moor, B., & Vandewalle, J. (2000). On the best rank-1 and rank-(r1, r2, …, rn) approximation of higher-order tensors. SIAM Journal on Matrix Analysis and Applications, 21(4), 1324–1342.CrossRefGoogle Scholar
  43. 43. Landsat 7 and landsat 8 data download mirror.
  44. 44. Ministry of Finance, Government of India. Retrieved June 12, 2016.
  45. 45.
  46. 46.
  47. 47.
    Singh, V. S., Pandey, D. N., & Chaudhry, P. (2010). Urban forests and open green spaces: Lessons for Jaipur, Rajasthan India. Jaipur: Rajasthan State Pollution Control Board.Google Scholar
  48. 48.
    Chu, E. (2016). The political economy of urban climate adaptation and development planning in Surat, India. Environment and Planning C: Government and Policy, 34(2), 281–298.CrossRefGoogle Scholar
  49. 49.
    Singh, P., Kikon, N., & Verma, P. (2017). Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustainable Cities and Society, 32, 100–114.CrossRefGoogle Scholar
  50. 50.
    Bhagat, R. B., & Dutta, T. (2018). Urban development, smart cities and displacement. In India migration report 2017 (pp. 98–116). Routledge India.Google Scholar
  51. 51.
    Lerman, R. I., & Yitzhaki, S. (1984). A note on the calculation and interpretation of the Gini index. Economics Letters, 15(3–4), 363–368.CrossRefGoogle Scholar
  52. 52.

Copyright information

© Korean Spatial Information Society 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceCochin University of Science and TechnologyKochiIndia
  2. 2.Department of Information TechnologyRajagiri School of Engineering and TechnologyKochiIndia

Personalised recommendations