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

TL;DR

This study utilizes Google Earth Engine and machine learning algorithms to perform multitemporal analysis for detecting urban changes in Cairo from 2013 to 2021, demonstrating the platform's effectiveness in change detection.

Contribution

It introduces a novel approach combining GEE, optical data, and ML algorithms for urban change detection over a large timeframe in a major city.

Findings

Effective identification of urban change and stability in Cairo.

Validation of GEE as a powerful tool for large-scale satellite data analysis.

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.