Harness the power of big spatial data with Earth Engine Python API and QGIS What you'll learn:
Students will access and sign up the Google Earth Engine Python API platform
Download, and install QGIS
Access satellite data in Earth Engine
Export geospatial Data
Access image collections
Learn to access and analyze various satellite data including, MODIS, Sentinel and Landsat
Cloud masking of Landsat images
Visualize time series images
Extract information from satellite data

Requirements
Basic understanding of GIS and Remote Sensing
Download and install QGIS
Access to the Google Earth Engine API

Description
Do you want to access satellite sensors using Earth Engine Python API?

Do you want to learn the QGIS Earth Engine plugin?

Do you want to visualize and analyze satellite data in Python?

Enroll in my new course to Python for Spatial Data Analysis with Earth Engine and QGIS.

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 QGIS and Earth Engine plugin. Then, you will have access to satellite data using the Python API.

What makes me qualified to teach you?

I am Dr. Alemayehu Midekisa, PhD. I am a geospatial data scientist, instructor and author. I have over 15 plus years of experience in processing and analyzing real big Earth observation data from various sources including Landsat, MODIS, Sentinel-2, SRTM and other remote sensing products. I am also the recipient of one the prestigious NASA Earth and Space Science Fellowship. I teach over 10,000 students on Udemy.

In this Python for Spatial Data Analysis with Earth Engine and QGIS course, I will help you get up and running on the Earth Engine Python API and QGIS. By the end of this course, you will have access to all example script and data such that you will be able to accessing, downloading, visualizing big data, and extracting information.

In this course we will cover the following topics:

Introduction to Earth Engine Python API

Install the QGIS Earth Engine Plugin

Load Landsat Satellite Data

Cloud Masking Algorithm

Calculate NDVI

Access Sentinel, Landsat, MODIS, CHIRPS, and VIIRS data

Export images and videos

Process image collections

CART classification

Clustering analysis

Linear regression

Global Land Cover Products (NLCD, and MODIS Land Cover)

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

Jump in right now to enroll. To get started click the enroll button.

Best,

Dr. Alemayehu Midekisa

Who this course is for
This course is meant for professionals who want to harness the power Google Earth Engine Python API and QGIS
People who want to understand various satellite image processing techniques using Python
Anyone who wants to learn accessing visualizing and extracting information from satellites
People who are working with satellite remote sensing data such as Landsat, MODIS, and Sentinel-2
Anyone who wants to apply for GIS or Remote Sensing Specialist job position

https://www.udemy.com/course/python-for-spatial-data-analysis-with-earth-engine-and-qgis

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