Integrating Remote Sensing and Machine Learning for Crop Classification in Latur Using Google Earth Engine and Random Forest

Landsat-8 based coastal ecosystem mapping in South Africa using random forest classification in Google Earth Engine

Soil Organic Carbon Mapping Through Remote Sensing and In Situ Data with Random Forest by Using Google Earth Engine: A Case Study in Southern Africa

This study, conducted within the SteamBioAfrica project, assessed the potential of Digital Soil Mapp

Detecting changes in mangrove forests along the Bintang Bolong Estuary, Gambia using Google Earth Engine, Sentinel-2 imagery and Random Forest classification

ELMehdiSELLAMI/Google-Earth-Engine-and-Machine-Learning-for-mapping-flash-flood-exposure-

Google Earth Engine and Machine Learning for mapping flash flood exposure - Case study: Tetouan, Mor

Land Use and Land Cover Classification in Google Earth Engine Using Sentinel-2 Based Random Forest: A Case Study of Katsina State, Nigeria

Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

Precipitation plays an important role in the food production of Southern Africa. Understanding the s