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Research Papers

Urban slum detection using texture and spatial metrics derived from satellite imagery

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References

  • Aguilar, M., Saldaña, M. and Aguilar, F., 2013. GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments. International Journal of Remote Sensing, 34 (7), 2583–2606.10.1080/01431161.2012.747018
  • Albrecht, F., Lang, S. and Hölbling, D., 2010. Spatial accuracy assessment of object boundaries for object-based image analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38 (4), C7.
  • Baatz, M. and Schäpe, A., 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: J. Strobl, ed. Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT-Symposium. Heidelberg: Herbert Wichmann Verlag, 12–23.
  • Baltsavias, E.P., 2004. Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems. ISPRS Journal of Photogrammetry and Remote Sensing, 58 (3–4), 129–151.10.1016/j.isprsjprs.2003.09.002
  • Belgiu, M. and Drǎgut, L., 2014. Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 67–75.10.1016/j.isprsjprs.2014.07.002
  • Belgiu, M., Drǎguţ, L. and Strobl, J., 2014a. Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 205–215.10.1016/j.isprsjprs.2013.11.007
  • Belgiu, M., Hofer, B. and Hofmann, P., 2014b. Coupling formalized knowledge bases with object-based image analysis. Remote Sensing Letters, 5 (6), 530–538.10.1080/2150704X.2014.930563
  • Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I. and Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58 (3–4), 239–258.10.1016/j.isprsjprs.2003.10.002
  • Cleve, C., Kelly, M., Kearns, F.R. and Moritz, M., 2008. Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems, 32 (4), 317–326.10.1016/j.compenvurbsys.2007.10.001
  • COI, 2001. Census of India [online]. Office of the Registrar General and Census Commissioner, Government of India. Available from: http://www.censusindia.gov.in/ [Accessed 24 June 2011].
  • Congalton, R., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37 (1), 35–46.10.1016/0034-4257(91)90048-B
  • Drǎguţ, L., Tiede, D. and Levick, S.R., 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24 (6), 859–871.
  • Ebert, A., Kerle, N. and Stein, A., 2009. Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data. Natural Hazards, 48 (2), 275–294.10.1007/s11069-008-9264-0
  • Haala, N. and Brenner, C., 1999. Extraction of buildings and trees in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing, 54 (2–3), 130–137.10.1016/S0924-2716(99)00010-6
  • Herold, M., Scepan, J. and Clarke, K.C., 2002. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environment and Planning A, 34 (8), 1443–1458.10.1068/a3496
  • Herold, M., Liu, X. and Clarke, K.C., 2003. Spatial Metrics and Image Texture for Mapping Urban Land Use. Photogrammetric Engineering & Remote Sensing, 69 (9), 991–1001.
  • Herold, M., Mayaux, P., Woodcock, C.E., Baccini, A. and Schmullius, C., 2008. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112 (5), 2538–2556.10.1016/j.rse.2007.11.013
  • Hofmann, P., 2001. Detecting informal settlements from Ikonos image data using methods of object oriented image analysis-an example from Cape Town (South Africa) In: Jürgens, C. ed. Remote sensing of urban areas. 41–42.
  • Hofmann, P., Strobl, J., Blaschke, T. and Kux, H., 2008. Detecting informal settlements from Quickbird data in Rio De Janeiro using an object based approach. In: T. Blaschke, S. Lang and G.J. Hay, eds. Object-Based Image Analysis. Berlin: Springer, 531–553.10.1007/978-3-540-77058-9
  • Hofmann, P., Blaschke, T. and Strobl, J., 2011. Quantifying the robustness of fuzzy rule sets in object-based image analysis. International Journal of Remote Sensing, 32 (22), 7359–7381.10.1080/01431161.2010.523727
  • Hu, X. and Weng, Q., 2010. Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method. Geocarto International, 26 (1), 3–20.
  • Kit, O., Lüdeke, M. and Reckien, D., 2012. Texture-based identification of urban slums in Hyderabad, India using remote sensing data. Applied Geography, 32 (2), 660–667.10.1016/j.apgeog.2011.07.016
  • Kohli, D., Sliuzas, R., Kerle, N. and Stein, A., 2012. An ontology of slums for image-based classification. Computers, Environment and Urban Systems, 36 (2), 154–163.10.1016/j.compenvurbsys.2011.11.001
  • Kohli, D., Warwadekar, P., Kerle, N., Sliuzas, R. and Stein, A., 2013. Transferability of Object-Oriented Image Analysis Methods for Slum Identification. Remote Sensing, 5 (9), 4209–4228.10.3390/rs5094209
  • Kuffer, M., Barros, J. and Sliuzas, R.V., 2014. The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery. Computers, Environment and Urban Systems, 48, 138–152.10.1016/j.compenvurbsys.2014.07.012
  • MASHAL, 2011. Pune city slum atlas. Pune: Maharashtra Social Housing and Action League.
  • Myint, S.W., Galletti, C.S., Kaplan, S. and Kim, W.K., 2013. Object vs. pixel: a systematic evaluation in urban environments. Geocarto International, 28 (7), 657–678.10.1080/10106049.2013.776642
  • Neuwirth, R., 2005. Shadow cities : a billion squatters, a new urban world. New York, NY: Routledge.
  • Novack, T. and Kux, H.J.H., 2010. Urban land cover and land use classification of an informal settlement area using the open-source knowledge-based system InterIMAGE. Journal of Spatial Science, 55 (1), 23–41.10.1080/14498596.2010.487640
  • Patino, J.E. and Duque, J.C., 2013. A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems, 37, 1–17.10.1016/j.compenvurbsys.2012.06.003
  • PMC, 2012. Integrated ward level disaster management plan. Pune: Pune Municipal corporation.
  • Pontius, R.G. and Millones, M., 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32 (15), 4407–4429.10.1080/01431161.2011.552923
  • Ridd, M.K., 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16 (12), 2165–2185.10.1080/01431169508954549
  • Scepan, J., 1999. Thematic validation of high-resolution Global Land-Cover Data Sets. Photogrammetric engineering and remote sensing, 65 (9), 1051–1060.
  • Shekhar, S., 2010. Applying GIS and RS for modeling the growth of slums in Pune City, Maharashtra, India. Thesis (M.Sc.). University of Salzburg.
  • Shekhar, S., 2012. Detecting slums from QuickBird data in Pune using an object-oriented approach. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39 (8), 519–524.
  • Sliuzas, R., Mboup, G. and Sherbinin, A., 2008. Expert group meeting on slum identification and mapping. Enschede, The Netherlands: ITC.
  • Stoler, J., Daniels, D., Weeks, J.R., Stow, D.A., Coulter, L.L. and Finch, B.K., 2012. Assessing the utility of satellite imagery with differing spatial resolutions for deriving proxy measures of slum presence in Accra, Ghana. GIScience & remote sensing, 49 (1), 31–52.
  • Stow, D., Lopez, A., Lippitt, C., Hinton, S. and Weeks, J., 2007. Object-based classification of residential land use within Accra, Ghana based on QuickBird satellite data. International Journal of Remote Sensing, 28 (22), 5167–5173.10.1080/01431160701604703
  • Stow, D.A., Lippitt, C.D. and Weeks, J.R., 2010. Geographic object-based delineation of neighborhoods of Accra, Ghana using QuickBird satellite imagery. Photogrammetric Engineering & Remote Sensing, 76 (8), 907–914.10.14358/PERS.76.8.907
  • Taubenböck, H. and Kraff, N.J., 2014. The physical face of slums: a structural comparison of slums in Mumbai, India, based on remotely sensed data. Journal of Housing and the Built Environment, 29 (1), 15–38.10.1007/s10901-013-9333-x
  • Trimble, 2014. Reference Book. eCognition Developer 9.0. München, Germany.
  • UN-HABITAT, 2006. Analytical perspective of pro-poor slum upgrading frameworks. Nairobi, Kenya: United Nations Human Settlements Programme.
  • UN-HABITAT, 2010. State of World’s Cities 2010/2011. UK and USA: United Nations Human Settlements Programme.
  • UN-HABITAT, 2014. Streets as tools for urban transformation in slums : A street-led approach to citywide slum upgrading. Nairobi, Kenya: United Nations Human Settlements Programme.
  • Van Coillie, F.M.B., Gardin, S., Anseel, F., Duyck, W., Verbeke, L.P.C. and De Wulf, R.R., 2014. Variability of operator performance in remote-sensing image interpretation: the importance of human and external factors. International Journal of Remote Sensing, 35 (2), 754–778.10.1080/01431161.2013.873152
  • Weeks, J., Hill, A., Stow, D., Getis, A. and Fugate, D., 2007. Can we spot a neighborhood from the air? Defining neighborhood structure in Accra, Ghana. GeoJournal, 69 (1-2), 9–22.10.1007/s10708-007-9098-4