Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery

@article{Duque2017ExploringTP,
  title={Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery},
  author={Juan Carlos Duque and Jorge E. Patino and Alejandro Betancourt},
  journal={Remote Sensing},
  year={2017},
  volume={9},
  pages={895}
}
Slum identification in urban settlements is a crucial step in the process of formulation of propoor policies. However, the use of conventional methods for slums detection such as field surveys may result time consuming and costly. This paper explores the possibility of implementing a low-cost standardized method for slum detection. We use spectral, texture and structural features extracted from very high spatial resolution imagery as input data and evaluate the capability of three machine… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 50 references

A feature extraction software tool for agricultural object-based image analysis

  • L. A. Ruiz, J. A. Recio, A. Fernández-Sarŕıa, T. Hermosilla
  • Computers and Electronics in Agriculture, 76(2…
  • 2011
Highly Influential
19 Excerpts

Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data

  • J. C. Duque, J. E. Patino, L. A. Ruiz, J. E. Pardo-Pascual
  • Landscape and Urban Planning, 135:11–21.
  • 2015
Highly Influential
10 Excerpts

Multiscale evaluation of an urban deprivation index: Implications for quality of life and healthcare accessibility planning

  • P. Cabrera-Barona, C. Wei, M. Hagenlocher
  • Applied Geography, 70:1–10.
  • 2016
Highly Influential
4 Excerpts

The physical face of slums: a structural comparison of slums in Mumbai, India, based on remotely sensed data

  • H. Taubenböck, N. J. Kraff
  • Journal of Housing and the Built Environment, 29…
  • 2014
Highly Influential
8 Excerpts

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