3.55 out of 5
3.55
48 reviews on Udemy

Python for Spatial Data Analysis with Earth Engine and QGIS

Harness the power of big spatial data with Earth Engine Python API and QGIS
Instructor:
Alemayehu Midekisa
312 students enrolled
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

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

Introduction to Earth Engine Python API

1
Sign Up on Earth Engine
2
Install Earth Engine Plugin in QGIS
3
Load Landsat Images
4
Calculate NDVI
5
Map Image Collection
6
Landsat Cloud Mask

Geospatial Data Visualization

1
Earth Observation Satellites
2
Landsat Visualization
3
MODIS Land Cover Visualization
4
NLCD Land Cover Visualization
5
NDVI Visualization
6
NDWI Visualization
7
Terrain Visualization

Access Raster Data Using Earth Engine Python API

1
Sentinel
2
CHIRPS
3
VIIRS Nighttime Light
4
MODIS NDVI

Images in Earth Engine Python API

1
Download
2
Clipping
3
Image Metadata

Machine Learning in Earth Engine Python API

1
Clustering
2
CART Classification

Advanced Algorithms

1
Spectral Unmixing
2
Linear Regression
3
Object-based Detection
4
SMAP Soil Moisture

Final Project

1
Final Project

Bonus Lecture

1
Bonus Lecture
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3 hours on-demand video
2 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion