A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis

Ivan Elias Ruiz Hernandez, Wen Zhong Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

Rapid urban growth in developing countries is causing a great number of urban planning problems. To control and analyse this growth, new and better methods for urban land use mapping are needed. This article proposes a new method for urban land-use mapping, which integrates spatial metrics and texture analysis in an object-based image analysis classification. A high-resolution satellite image was used to generate spatial and texture metrics from the machine learning algorithm of Random Forests landcover classification. The most meaningful spatial indices were selected by visual inspection and then combined with the image and texture values to generate the classification. The proposed method for land-use mapping was tested using a 10-fold crossvalidation scheme, achieving an overall accuracy of 92.3% and a kappa coefficient of 0.896. These steps produced an accurate model of urban land use, without the use of any census or ancillary data, and suggest that the combined use of spatial metrics and texture is promising for urban land-use mapping in developing countries. The maps produced can provide the landuse data needed by urban planners for effective planning in developing countries.
Original languageEnglish
Pages (from-to)1175-1198
Number of pages24
JournalInternational Journal of Remote Sensing
Volume39
Issue number4
DOIs
Publication statusPublished - 16 Feb 2018

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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