Complete Google Earth Engine for Remote Sensing & GIS
What you'll learn
- Students will gain access to and a thorough knowledge of the Google Earth Engine platform
- Carry out pre-processing and processing of satellite data in the cloud
- Implement some of the most common GIS techniques on satellite data
- Implement time series analysis of multi-temporal optical data
- Implement machine learning algorithms on satellite data
- Desire to learn satellite data processing using Google Earth Engine
- Desire to learn common pre-processing and GIS techniques in Google Earth Engine
- Prior knowledge of the different common GIS data types
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BASIC SATELLITE REMOTE SENSING AND GIS ANALYSIS USING GOOGLE EARTH ENGINE (GEE).
Are you currently enrolled in any of my GIS and remote sensing related courses?
Or perhaps you have prior experience in GIS or tools like R and QGIS?
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 endeavor 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; 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 paltform . 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 am an Oxford University MPhil (Geography and Environment) graduate. 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.
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 very expensive, and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at arisk of prosecution), you will learn to carry out some of the most important and common remote sensing analysis tasks using one of the most powerful earth observation data and analysis platform. GEE is rapidly demonstrating its importance in the geo-spatial 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 important and widely encountered remote sensing data processing and 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:
- People who wish to harness the power of cloud computing and Google Earth Engine for processing satellite data
- People wanting to learn about satellite imagery processing and deriving insights from these data
- People wanting to work with long term temporal long term optical satellite images such as MODIS
- People wanting to implement machine learning algorithms on satellite data
- Students of forestry, environment, geography and environmental sciences
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).