Introduction to Google Earth Engine Python API for Machine Learning based Classification - Workshop

Welcome to the most comprehensive workshop on using the Google Earth Engine Python API for machine learning-based classification! If you're interested in remote sensing, geospatial analysis, or machine learning applied to Earth observation data, this workshop is a must-watch.
In the first half of the workshop, I provide a solid foundation by discussing the background of Google Earth Engine and its Python API. I also cover the benefits of using Python for Earth Engine, explore various learning resources, and guide you through setting up your development environment for seamless coding.
The latter part of the workshop is dedicated to a hands-on coding session, where I showcase practical examples using Google Colab. This workshop will walk you through the process of implementing supervised classification techniques in Earth Engine, leveraging machine learning algorithms to extract valuable insights from satellite imagery.
Topics Covered:
Why use the Python API for Google Earth Engine?
Learning the Earth Engine Python API
Setting up your environment
Machine Learning Overview
Supervised Classification in Earth Engine
Don't forget to check out the workshop materials and code examples on my GitHub repository. The links to all materials and sources mentioned in the workshop are available below:
Github Project Repo: github.com/wal...
My Webpage: waleedgeo.com/
My Publications: waleedgeo.com/...
Slides PDF: github.com/wal...
#GoogleEarthEngine #PythonAPI #MachineLearning #RemoteSensing #GeospatialAnalysis #GEE #geemap #google #geospatial #python #cloudcomputing #machinelearning #supervisedlearning #landuse #lulc #landuselandcover #waleedgeo #mirzawaleed #bigdata #tutorial #tutorials

[@Khalilgholamnia2]

very excellent, thank you Sir

[@shafqathussain2216]

Very Excellent Sir ♥

[@mez5554]

Very insightful. I have read fully 3 of your papers and they are very resourceful. Could you please do a comprehensive analysis of fusing Optical and SAR in Python API? Especially MODIS & ALOS PALSAR?

[@mwaleedgeo]

Thank you for the suggestion. I will add this on my to do list.

[@hardworker-p3j]

Excellent explanation
Sir I have a doubt if I give any different area image to this ml model will it work???

[@REAPER7777]

Absolutely loved the tutorial. Please make a video on how to create those maps that you use in your articles.🙏

[@mwaleedgeo]

Thanks, can you please provide details of what do you meant by 782437?

[@REAPER7777]

@@mwaleedgeo I wrote " maps" in my comment, I don't know why the number popped up!

[@mwaleedgeo]

I get it now, that is on my to do list for future!

[@dipsonbhandari6925]

Interesting. Is there any specific courses on Google earth Engine that you can recommend for someone who is just starting off?

[@mwaleedgeo]

Try this, it is open sourced
spatialthoughts.com/courses/google-earth-engine/

[@vineesh.geography]

Good

[@giani9042]

Thank you very much for the tutorial, sir. I would like to ask you something, so I am planning to classify land cover using random forest model with 500 tree in GEE. Unfortunately, GEE can't process the code since the computation is limited (fyi I'm using the free version of GEE). Would this kind of problem happen if I'm using GEE API in python?

[@fabiooliveira7535]

Hello,
In addition to the images, can I download climate data for a certain region to be processed? Another question, is this data generated in real time?

[@programmingholic]

bro kindly create a playlist for this we can bookmark it

[@mwaleedgeo]

Currently I am working on designing a comprehensive course on data science using earth engine. After a few video uploads, I will create the playlist and will share.

[@sharminfarzana5717]

Can I have your dataset?