Google Earth Engine (GEE) for Remote Sensing and GIS Analysis using JavaScript and Python APIs integration with QGIS and ArcGIS Pro

Mode: Live Interactive Sessions (Online)
Duration: 7 Days
Session Length: 4 Hours per Day
Level: Beginner → Advanced

Course Fee: 600 USD

🎯 Special Early-Bird Offer:
The first 10 registered participants will receive an exclusive 50% discount.

💰 Discounted Fee: Only 300 USD

To claim your 50% discount, contact via WhatsApp: +8801780942798 or Email: rmijanur10266@gmail.com

⚠️ Limited to the first 10 registrations only. Once the seats are filled, the standard fee of 600 USD will apply.

Register early and secure your spot at half the price.

Join our upcoming online live training program starting on 16th May, 2026, where you will gain hands-on experience in using cloud-based geospatial processing with Google Earth Engine, seamlessly connected with desktop GIS platforms.

Registration is now open for the 39th Batch of 7-days comprehensive online live training on Google Earth Engine (GEE) for Remote Sensing and GIS Analysis using JavaScript and Python APIs integration with QGIS and ArcGIS pro, tailored for beginners to advanced learners. Designed specifically for non-coders, this course empowers you with advanced geospatial skills—no prior programming knowledge is required!

These classes will teach you all the necessary things to start using GEE for your remote sensing analysis. We primarily focus on individuals who are unfamiliar with programming languages and the Earth Engine function. We cover LULC mapping, Change detection Analysis, Air quality Monitoring, Time series analysis, calculating any Indices, Supervised Classification, Unsupervised Classification, Machine Learning Methods, NDVI change detection, and more.

📅 Day 1 — Foundations of Google Earth Engine

Date: 16 May 2026

• Introduction to Google Earth Engine

• Setting up Python & JavaScript API for GEE

• Using GEE JavaScript and Python APIs

• Basics of JavaScript Syntax and Python for GEE

• Understanding Client vs. Server Objects

• Executing Code on the GEE Server

• Importing Raster & Vector Data (Local + GEE Datasets)

• Filtering Attribute Tables

• How to use GEE JavaScript and Python API with QGIS and ArcGIS pro

📅 Day 2 — Satellite Data Processing & Visualization

Date: 22 May 2026

• Filtering and Displaying Satellite Imagery (Landsat, Sentinel, MODIS)

• Creating Satellite Composites

• Band Combination Techniques

• Exporting Satellite Imagery

• Raster Data Processing: Import, Filter, Reduce, Clip & Display

• NDVI Time Series Analysis using Ready-made Datasets

• Exporting Shapefiles

• Annual Image Composite Generation

📅 Day 3 — Environmental Monitoring & Thermal Analysis

Date: 23 May 2026

• Calculating Any Indices from Satellite Images using Landsat and Sentinel

• Filtering and Displaying Satellite Images: Sentinel-2 and Monitoring NDWI, NDVI

•  Extract water body using Thresholding

•  NDVI, NDWI , SAVI, and all indices Time series Chart using Landsat and Sentinel

• Export Any Shapefile from GEE

•  How to add Gradient Legend and Title on GEE

•  NDWI Calculated from Modis and Landsat data

📅 Day 4 — Spectral Indices & Flood Mapping

Date: 5 June 2026

•  How to remove cloud and Haze from satellite imagery- Landsat and Sentinel
•  Visualization (DEM) of Hill shade and Slope Map in GEE using NASA SRTM and Aster
•  Land surface temperature (LST) monitoring from Landsat satellite imagery and Modis
•  How to calculated Average, Maximum, Minimum NDVI any specific region
•  GEE: How to make monthly Evapotranspiration

📅 Day 5 — Air Quality & Atmospheric Monitoring

Date: 6 June 2026

• Air Quality Monitoring (All Major Parameters)

• Downloading Time-Series Data in CSV Format

• Air Quality Visualization & Trend Charts

• Emission Analysis (NOx & Other Gases) using Sentinel-5

• Aerosol Optical Depth (AOD) Monitoring

• PM2.5 Analysis

• Research Map Creation using GEE & ArcMap

• Accuracy Assessment Techniques

• Extracting Pixel Values for Specific Locations

• Trend Analysis & Linear Regression Modeling

📅 Day 6 — Machine Learning Applications in GEE

Date: 12 June 2026

• Introduction to Machine Learning in Remote Sensing

• LULC Mapping using Supervised & Unsupervised Methods

• Algorithms: Random Forest, CART, SVM, Minimum Distance

• Accuracy Assessment (Kappa, Producer’s & User’s Accuracy)

• Calculating LULC Class Areas

• Adding Legends to LULC Maps

• Exporting Results for Research Publication Maps

📅 Day 7 — Change Detection & Advanced Modeling

Date: 13 June 2026

• Land Use / Land Cover Change Detection

• NDVI Change Detection Analysis

• NDVI Classification Techniques

• Dense Vegetation Extraction using Thresholding

• Class-wise LULC Change Detection in a Single Layer

• Transition Matrix & Area Calculation

• Urban Growth Monitoring

• Hyperparameter Tuning for Machine Learning Accuracy

🎯 What You Will Gain

✔ Strong command over Google Earth Engine workflows
✔ Hands-on experience with satellite datasets (Landsat, Sentinel, MODIS)
✔ Environmental, climate, and urban analysis skills
✔ Machine learning-based land cover classification
✔ Research-ready outputs for academic or professional use
✔ Practical expertise required in GIS, Remote Sensing, and Data Science careers