Advanced Google Earth Engine(GEE) For Spatial Data Analysis
What you'll learn
- Gain robust grounding in the basic and latest features of Google Earth Engine (GEE)
- Learn how to work with in-built shapefiles and imagery data present within GEE
- Learn to upload and analyse your own data in GEE
- Gain proficiency in JavaScript for operating the GEE ecosystem
- Carry out pre-processing and processing of satellite data in the GEE cloud
Requirements
- Desire to learn spatial data processing using Google Earth Engine
- Desire to learn common image pre-processing and GIS techniques in Google Earth Engine
- Prior knowledge of the different common spatial data types
- Access to a gmail email account to sign up for Google Earth Engine
Description
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BASIC SATELLITE REMOTE SENSING AND GIS ANALYSIS USING THE GOOGLE EARTH ENGINE (GEE).
Are you currently enrolled in any of my GIS and remote sensing related courses?
Or perhaps you have prior experiences in GIS or tools like R and QGIS?
You want to quickly analyse large amounts of geospatial data
Implement machine learning models on remote sensing data
You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis?
The next step for you is to gain proficiency in satellite remote sensing data analysis and GIS using GEE, a cloud-based endeavour by Google that can help process several petra-byte of imagery data.
MY COURSE IS A HANDS-ON TRAINING WITH REAL REMOTE SENSING AND GIS DATA ANALYSIS WITH GOOGLE EARTH ENGINE- A planetary-scale platform for Earth science data & analysis; including implementing machine learning models on imagery data, powered by Google's cloud infrastructure. !
My course provides a foundation to carry out PRACTICAL, real-life remote sensing and GIS analysis tasks in this powerful cloud-supported platform. By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis.
Why Should You Take My Course?
I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real-life spatial remote sensing data from different sources and producing publications for international peer-reviewed journals.
In this course, actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA will be used to give a practical hands-on experience of working with remote sensing and understanding what kind of questions remote sensing can help us answer. You will be introduced to a variety of other datasets as well, including those relating to fires and socio-economic measures.
This course will ensure you learn & put remote sensing data analysis into practice today and increase your proficiency in geospatial analysis.
Remote sensing software tools are costly, and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at risk of prosecution), you will learn to carry out some of the most critical and common remote sensing analysis tasks using one of the most powerful earth observations data and analysis platform. GEE is rapidly demonstrating its importance in the geospatial sector and improving your skills in GEE will give you an edge over other job applicants..
This is a fairly comprehensive course, i.e. we will focus on learning the most essential and widely encountered remote sensing data processing and GIS analysis techniques in Google Earth Engine
You will also learn about the different sources of remote sensing data there are and how to obtain these FREE OF CHARGE and process them using within GEE.
In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!
ENROLL NOW :)
Who this course is for:
- Students of forestry, environment, geography and environmental sciences
- People interested in geospatial data analysis
- People interested in geospatial imagery analysis
- People interested in applying machine learning techniques on geospatial data
Instructor
![Minerva Singh](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.5 Instructor Rating
- 16,903 Reviews
- 85,192 Students
- 47 Courses
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).