Spatial Data Analysis in Google Earth Engine Python API
What you'll learn
- Students will access and sign up the Google Earth Engine Python API platform
- Access satellite data in Earth Engine
- Export geospatial Data including rasters and vectors
- Access images and image collections from the Earth Engine cloud data library
- Perform cloud masking of various satellite images
- Visualize and analyze various satellite data including, MODIS, Sentinel and Landsat
- Visualize time series images
- Run machine learning algorithms using big Earth Observation data
Requirements
- This course has no requirements.
Description
Do you want to access satellite sensors using Earth Engine Python API and Jupyter Notebook?
Do you want to learn spatial data science on the cloud?
Do you want to become a spatial data scientist?
Enroll in my new course Spatial Data Analysis in Google Earth Engine Python API.
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 Anaconda and Jupyter Notebook. Then, you will have access to satellite data using the Earth Engine Python API.
In this Spatial Data Analysis with Earth Engine Python API course, I will help you get up and running on the Earth Engine Python API and Jupyter Notebook. By the end of this course, you will have access to all example scripts and data such that you will be able to access, download, visualize big data, and extract information.
In this course, we will cover the following topics:
Introduction to Earth Engine Python API
Install the Anaconda and Jupyter Notebook
Set Up a Python Environment
Raster Data Visualization
Vector Data Visualization
Load Landsat Satellite Data
Cloud Masking Algorithm
Calculate NDVI
Export images and videos
Process image collections
Machine Learning Algorithms
Advanced digital image processing
One of the common problems with learning image processing is the high cost of software. In this course, I entirely use open source software including the Google Earth Engine Python API and Jupyter Notebook. All sample data and scripts will be provided to you as an added bonus throughout the course.
Jump in right now and enroll.
Who this course is for:
- This course is meant for professionals who want to harness the power Google Earth Engine Python API and Jupyter Notebook
- People who want to understand various satellite image processing techniques using Python and Jupyter Notebook
- Anyone who wants to learn accessing and extracting information from Earth Observation data
- Anyone who wants to apply for a spatial data scientist job position
Instructor
- 4.0 Instructor Rating
- 1,898 Reviews
- 21,621 Students
- 20 Courses
Dr. Alemayehu Midekisa is a geospatial data scientist with over 15 years of professional experience in academia and industry. His research focus is on leveraging geospatial AI, big Earth observation data, and cloud computing to monitor environmental changes. Dr. Midekisa is particularly interested in applying machine learning models, large-scale remote sensing data, and cloud computing such as Google Earth Engine to quantify land surface changes including land cover changes, urbanization trends, disease outbreaks, and surface water dynamics.