Spatial Data Analysis in Google Earth Engine Python API

Learn machine learning, big data analysis, GIS, remote sensing with Earth Engine Python API and Jupyter Notebook

3.60 (85 reviews)
Udemy
platform
English
language
Programming Languages
category
470
students
2.5 hours
content
Mar 2024
last update
$49.99
regular price

What you will learn

Students will access and sign up the Google Earth Engine Python API platform

Access satellite data in Earth Engine

Export geospatial Data including rasters and vectors

Access images and image collections from the Earth Engine cloud data library

Perform cloud masking of various satellite images

Visualize and analyze various satellite data including, MODIS, Sentinel and Landsat

Visualize time series images

Run machine learning algorithms using big Earth Observation data

Description

Do you want to access satellite sensors using Earth Engine Python API and Jupyter Notebook?

Do you want to learn spatial data science on the cloud?

Do you want to become a spatial data scientist?


Enroll in my new course Spatial Data Analysis in Google Earth Engine Python API.


I will provide you with hands-on training with example data, sample scripts, and real-world applications. By taking this course, you be able to install Anaconda and Jupyter Notebook. Then, you will have access to satellite data using the Earth Engine Python API.


In this Spatial Data Analysis with Earth Engine Python API course, I will help you get up and running on the Earth Engine Python API and Jupyter Notebook. By the end of this course, you will have access to all example scripts and data such that you will be able to access, download, visualize big data, and extract information.


In this course, we will cover the following topics:

  • Introduction to Earth Engine Python API

  • Install the Anaconda and Jupyter Notebook

  • Set Up a Python Environment

  • Raster Data Visualization

  • Vector Data Visualization

  • Load Landsat Satellite Data

  • Cloud Masking Algorithm

  • Calculate NDVI

  • Export images and videos

  • Process image collections

  • Machine Learning Algorithms

  • Advanced digital image processing


One of the common problems with learning image processing is the high cost of software. In this course, I entirely use open source software including the Google Earth Engine Python API and Jupyter Notebook. All sample data and scripts will be provided to you as an added bonus throughout the course.


Jump in right now and enroll.

Content

Introduction to Earth Engine Python API

Install Anaconda
Set Up Python Environment
Sign Up on Earth Engine
Install Earth Engine Python API
Load Landsat Images

Raster Data Visualization

Landsat Visualization
MODIS Land Cover
NLCD Land Cover
NDVI Visualization

Vector Data Visualization

US States
USA Counties
International Boundary

Raster Data Analysis: Images

Clipping
Image Metadata
Band Math
Calculate MODIS NDVI

Raster Data Analysis: Image Collection

Clip Image Collection
Landsat Simple Composite
Filter by Calendar Day of Year

Machine Learning: Unsupervised and Supervised Classification

Clustering: Unsupervised Classification
CART: Supervised Classification
SVM: Supervised Classification

Bonus Lecture

Bonus Lecture

Screenshots

Spatial Data Analysis in Google Earth Engine Python API - Screenshot_01Spatial Data Analysis in Google Earth Engine Python API - Screenshot_02Spatial Data Analysis in Google Earth Engine Python API - Screenshot_03Spatial Data Analysis in Google Earth Engine Python API - Screenshot_04

Reviews

Stacie
November 25, 2022
This course uses geehydro which is no longer supported and has been widely replaced with geemap. The code needs to use geemap to be current, and much of the code is not reproducible without geehydro. I am now following the training using the instructors collab course, but then I find out this course (with much the same instruction) is non refundable.
Hadi
August 12, 2022
I thought this course would be helpful, but I'm fully disappointed. What is it, is this a movie or something? We need an explanation over codes, why did you do that, do this? Just showing some lines of code and that's it?!!!! I believe this course should not be here and it is more useful for youtube or something. I'm fully disappointed.
Stefano
June 16, 2022
I had difficulties in installation following the lectures, i had to install everything following another tutorial, but everything else is just what i wanted.
Timothy
May 16, 2022
It clearly shows how to work with the EE, but I was hoping for more information about application and little more description of functions and their documentation.
Sabbaha
May 3, 2021
Thank you so much, this course was quite help me to understanding spatial data analysis and programming language. I am so interested with them of both
Cédrick-Armel
April 21, 2021
Pour un prix initial de plus de 100 EUR la formation n'est pas assez consistante. On a fait que afficher des cartes dans ce cours. L'auteur devrait faire évoluer le cours vers l'analyse à proprement dit des données satellitaires.
Eduardo
February 24, 2021
É um curso introdutório. Porém, faz uma boa apresentação da utilização do Jupyter para processar imagens, utilizando o Google Earth Engine.
Tatag
January 27, 2021
This course is very useful for me who is an academic in the field of spatial analysis. thank you for providing very useful material
Leila
January 15, 2021
The course was repetitive throughout the sessions, mainly repeating the easiest parts of starting the environment and packages.. I would say the second half of the lecture was more informative and helpful.
Ronald
August 16, 2020
Was awesome, we can see new topics about remote sensing using Python, it´s great for the remote sensing community. With this course I learned new experience, because in my country few people are using Python for remote sensing on the GEE platform, this will allow me to be one step forward from them, in fact, I will use for my thesis and for my job.

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Related Topics

2804191
udemy ID
2/9/2020
course created date
8/19/2020
course indexed date
nawidrasooly
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