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.

翻译：本研究旨在利用Google Earth Engine (GEE)平台，通过光学数据与特定机器学习算法进行多时相分析，以实现城市区域变化检测。以埃及开罗市为典型案例——该城市在过去十年间位列全球人口最多的五大特大城市之一。研究时段覆盖2013年7月至2021年7月，对感兴趣区域实施了分类及变化检测分析。结果表明，所提出方法在识别选定时段内城市区域的已变化/未变化区域方面具有有效性。此外，本研究旨在论证GEE作为高效云解决方案在处理海量卫星数据方面日益增长的重要性。