How to Set Up Your Google Earth Engine–Enabled Cloud Project (For Non-Commercial Users)

Registration is now open for the 36th Batch of 7-day comprehensive online live training on Google Earth Engine (GEE) for Remote Sensing and GIS Analysis using JavaScript and Python APIs, 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.

📅 Class Start: 20th December 2025
⏳ Admission Deadline: 19th December 2025
🎉 Special Offer: First 10 registrants get 50% off – Book your seat now!

Class Schedule:
Duration: 7 days (Fridays & Saturdays in a week)
Time: 9:00 PM – 1:00 AM (GMT +6)
Language: English

Online Training Benefits
Ø * Course Certificate (After submitting all Assignments)
Ø * Materials (Slide, PDF)
Ø * Practice Code (All codes provide)
Ø * Recorded Class (All classes recorded video provided)
Ø * Lifetime teaching support


For more information about registration, visit our website: https://www.studyhacksgeospatial.com/google-earth-engine/

For Registration, Contact this Email: rmijanur10266@gmail.com
Or WhatsApp 24/7: +8801780942798 or https://wa.me/8801780942798
Course Content:
1st day:
 Introduction to GEE
 How to use GEE JavaScript and Python API
 Learn the basic principles of JavaScript syntax and python
 Client vs. Server object on GEE
 How you get the server to execute your code?
 Importing Raster and Vector Data: Local storage & GEE Dataset
 Filtering Attribute Table
2nd day:
 Filtering and Displaying Satellite Images: Landsat , Sentinel
 Satellite Composite
 Band combinations
 Export Satellite Imagery: Landsat , Sentinel, and Modis
 Import, Filter, Reduce, Clip, and display Raster data in GEE
 Time series Chart of NDVI using the GEE readymade dataset
 Export Any Shapefile

3rd day
 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

4th day:
 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

5th day:
 Air Quality Monitoring: all prameters
 How to Download Air Quality parameters Time series data in CSV format using GEE
 Air Quality Monitoring Time Series chart
 Air Quality Monitoring: How to calculate total emission of nitrogen-oxide or any gases in GEE using
sentinel-5p
 ArcMap software: How to make research paper map using GEE & ArcMap software

6th day:
 Introduction to Machine Learning in GEE
 How to make LULC Map using Machine Learning: Supervised and Unsupervised algorithm
 Random forest, CART, SVM, Minimum distance classifier to make LULC
 How to Check LULC accuracy assessment using GEE. (Kappa, Producers & Consumers accuracy)
 Calculate LULC classes Area
 How to add Legend in LULC Map
 How to Export LULC and make research paper LULC map using ArcMap

7th day:
 Land-Use and Land-Cover Change Detection using GEE
 NDVI change detection using GEE
 Class-wise LULC change detection in ONE layer using GEE
 Hyper parameter Tuning for improving the accuracy of your machine learning model

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