Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms

Mariapia Rita Iandolo, Francesca Razzano, Chiara Zarro, G. S. Yogesh, Silvia Liberata Ullo
8/22/2023
cs.CVeess.IV

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Abstract

The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and specific machine learning (ML) algorithms. As a case study, Cairo City has been identified, in Egypt country, as one of the five most populous megacities of the last decade in the world. Classification and change detection analysis of the region of interest (ROI) have been carried out from July 2013 to July 2021. Results demonstrate the validity of the proposed method in identifying changed and unchanged urban areas over the selected period. Furthermore, this work aims to evidence the growing significance of GEE as an efficient cloud-based solution for managing large quantities of satellite data.

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Cite this paper

@article{iandolo2023multitemporal,
  title  = {Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms},
  author = {Mariapia Rita Iandolo and Francesca Razzano and Chiara Zarro and G. S. Yogesh and Silvia Liberata Ullo},
  year   = {2023},
  eprint = {2308.11468},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.11468v1}
}

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