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
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.