4.33 (39 reviews)
☑ Students will gain access to and a thorough knowledge of the Google Earth Engine platform
☑ Learn how to obtain satellite data, apply image preprocessing for Landsat and Sentinel data in in Google Earth Engine
☑ Learn how import and export spatial data (vector and rsater) from / into the platform
☑ Run analyisis for geospatial applications on the cloud
☑ You'll have a copy of the codes used in the course for your reference
☑ Learn how to calculate spectral indices, create maximim composites and work with Big data on cloud
☑ Apply geospatial analysis for real practical example: flood mapping with Sentinel 2 images
☑ Learn image classification (land cover mapping) basics in Earth Engine
Google Earth Engine for Geospatial Analysis: 3 Courses in 1
This course is designed to take users who use GIS for basic geospatial data/GIS/Remote Sensing analysis to perform geospatial analysis tasks with Big Data on the cloud! This course provides you with all the necessary knowledge to start and advance your skills with Geospatial analysis and includes more than 5 hours of video content, plenty of practical analysis, and downloadable materials. After taking this course, you will be able to implement PRACTICAL, real-life spatial geospatial analysis, and tasks with the Big Data on the cloud.
You will learn how to import / export data to Earth Engine, how to perform arithmetical image calculation, how to map functions over image collections, and do iterations. We will cover Sentinel and Landsat image pre-processing and analyses for such applications as drought monitoring, flood mapping, and land cover unsupervised and supervised (machine learning algorithms such as Random Forest) classification.
One important part of the course is the practical exercises. You will be given some precise instructions, codes, and datasets to create for geospatial analysis in Google Earth Engine.
INCLUDED IN THE COURSE: You will have access to all the data used in the course, along with the Java code files. You will also have access to future resources. Enroll in the course today & take advantage of these special materials!
Introduction to Google Earth Engine
Why to work with Google Earth Engine?
Lab: Sign up for Google Earth Engine
Interface of Google Earth Engine: Code Editor & Explorer
Short introduction to spatila and satellite data . theory
Types of spatial data: vector and raster data
Introduction to raster data (satellite images)
Difference between sensors and platforms
Introduction to Landstat Program of NASA
Introduction to Sentinel Program of ESA
Overview of datasets in Earth Engine
Getting started with JavaScrip and geospatial analysis in Google Earth Engine
Lab: Mapping and Reducing Collection - Landsat Example
Lab: Working with image collections and image visualization
Lab: Image visualisation
Section 4: Practical Task
Image Calculations and Mapping Functions in Earth Engine
Introduction to image data: Landsat
Lab: Image Calculations Part 1 - Single Image Calculations
Lab: Image Calculations Part 2 - Create a composite and calculate NDVI
Lab: Calculate Zonal Statistics in Earth Engine
Lab: Short introduction to functions - Maximum NDVI Example
Lab: How to map a function over an image collection: Example of Landsat and NDVI
Lab: How to change default names for output image collection
Importing / Exporting Data in Google Earth Engine
Lab: Export image data from Google Earth Engine: an Introduction
Lab: Importing ratser and vector files into Google Earth Engine
Lab: Image mosaicking, clipping, reprojecting and exporting as tiff to Drive
Section 6 - Practical Task
Examples: Geospatial Analysis in Google Earth Engine
How to work with spatial data and remote sensing images - theory
Lab: Iterating function over Image Collection - Example of Drought Monitoring
Lab: Image preprocessing - Cloud masking of Sentinel 2 images
Normalized Difference Water Index for flood monitoring - thoery
Lab: Flood Mapping with Sentinel-2 and NDWI
Your Project - Flood Mapping
Introduction to Land use / land cover (LULC) classification
Land use land cover mapping - overview
Supervised classification with Google Earth Engine (explorer)
Unsupervised Image Classification and Image Compositing
Supervised land use mapping with Google Earth Engine and Random Forest
Task: Image Classification
The course is a good match but I would still suggest the author to work on delivering the concepts first before explaining the code.
This is a great course. The content is very helpful for understanding remote sensing in Earth Engine, and this is one of the best course you can take to learn remote sensinggeospatial analysis & Remote Sensing.
Thank you for this amazing course in Google Earth Engine that helped me learn a lot about Remote sensing fundamentals! What I liked most about this course is that it gives an overall look on Remote Sensing in a simple and easy way and also the fact that it is very practical!!
Great course that I found very informative. However, there were duplicate videos from your previous course on GEE which was disappointing because I was hopping to be challenged in new ways to develop and refine my skills. Great course in general and will absolutely to future courses with you relating to GIS and GEE.
I wish resource availability and organization of the course can be improved in the near future. Other than that, this was a good course, I learned a lot!
Excellent instruction. I tried many instructors to refresh my concepts and learn new things, not that other instructors were bad, some of them were very good too, but this course structured with the Questions and Solutions codes, to the point material with targeted and simple approach appealed to my learning style, instruction pace was excellent. This course motivated me to enroll in other courses by the same instructor.