Extraction of Urban Quality of Life Indicators Using Remote Sensing and Machine Learning: The Case of Al Ain City, United Arab Emirates (UAE)

Mohamed M. Yagoub, Yacob T. Tesfaldet, Marwan G. Elmubarak, Naeema Al Hosani

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Urban quality of life (UQoL) study is very important for many applications such as services distribution, urban planning, and socioeconomic analysis. The objective of this study is to create an urban quality of life index map for Al Ain city in the United Arab Emirates (UAE). The research aligns with the United Nations Sustainable Development Goals number ten (reduce inequalities) and eleven (sustainable cities and communities). In this study, remote sensing images and GIS vector datasets were used to extract biophysical and infrastructure facility indicators. The biophysical indicators are normalized difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), soil adjusted vegetation index (SAVI), enhanced normalized difference impervious surfaces index (ENDISI), normalized difference built-up index (NDBI), land surface temperature (LST), slope, and land use land cover (LULC). In addition, infrastructure facility indicators such as distances to main roads, parks, schools, and hospitals were obtained. Additional infrastructure facility variables namely built-up to green area and build-up to bare soil area ratio were extracted from the LULC map. Machine learning was used to classify satellite images and generate LULC map. Random Forest (RF) was found as the best machine learning classifier for this study. The overall classification and Kappa hat accuracy was 95.3 and 0.92, respectively. Both biophysical and infrastructure facility indicators were integrated using principal component analysis (PCA). The PCA analysis identified four components that explain 75% of the variance among the indicators. The four factors were interpreted as the effect of LULC, infrastructure facility, ecological, and slope. Finally, the components were assigned weights based on the percentage of variance they explained and developed the UQoL map. Overall, the result showed that greenness has a greater effect on the spatial pattern of UQoL in Al Ain city. The study could be of a value to policy makers in urban planning and socioeconomic departments.

Original languageEnglish
Article number458
JournalISPRS International Journal of Geo-Information
Volume11
Issue number9
DOIs
Publication statusPublished - Sept 2022

Keywords

  • greenness
  • machine learning
  • principal component analysis (PCA)
  • remote sensing
  • urban quality of life

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)

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