Cloud-based large-scale data retrieval, mapping, and analysis for land monitoring applications with Google Earth Engine (GEE)

Abstract

Cloud-based computing systems are linked with analytical tools for large-scale flood monitoring to solve these problems. Researchers want an exceptionally efficient and resilient geospatial framework with advanced algorithms for immediate results from the examination of large datasets. The study uses web-based analysis to demonstrate the potential of Google Earth Engine (GEE) for geospatial-analytical processes in flood-affected areas and to comprehend the socio-demographic ramifications. Surface water mapping is done using a histogram-based threshold method. The study examines how to analyse Sentinel-1 SAR data for automated flood mapping and how to validate results using data from the optical sensor Sentinel-2. Furthermore, using the Google Earth Engine platform, this study focuses on cloud-based large-scale flood data mapping. The research combines geographic information with advanced data processing techniques, algorithms, and web-based platforms to produce encouraging results and monitor real-time flooding occurrences for significant planning and decision-making. The research effectively assesses the importance of cloud-based data processing for the performance evaluation of algorithms in a cloud-based platform for monitoring real-time issues. The study's findings are useful for analysing surface water mapping applications.

Keywords

• COVID-19
• Sentinel-1A
• Cloud Based
• Google Earth Engine (GEE)
• Land Monitoring