Spatial Data Analysis with 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
Course content
- Preview03:18
- 08:28Set Up Python Environment
- 01:46Sign Up on Earth Engine
- 10:19Install Earth Engine Python API
- Preview08:01
Requirements
- Download and Install Anaconda and Jupyter Notebook
- Basic understanding of GIS and Remote Sensing
- Access to the Google Earth Engine API
Description
Do you want to access satellite sensors using Earth Engine Python API and Jupyter Notebook?
Do you want to learn the spatial data science on the cloud?
Do you want to become a spatial data scientist?
Enroll in my new course to Spatial Data Analysis with Earth Engine 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.
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 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 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 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 script will be provided to you as an added bonus throughout the course.
Jump in right now and enroll.
Best,
Dr. Alemayehu Midekisa, PhD
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
![Alemayehu Midekisa](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 3.9 Instructor Rating
- 1,527 Reviews
- 13,123 Students
- 13 Courses
Dr. Alemayehu Midekisa, PhD is an applied remote sensing scientist with 15 plus years of expertise in big Earth observation data and various methods such as machine learning, time series analysis, deep learning, and cloud computing. He is proficient in different scripting languages including Python, JavaScript, R, and Google Earth Engine. He is a former NASA Earth and Space Science fellow. With global experience in USA, Europe and Africa, his research focus is in the application of multi-sensor remote sensing data utilizing Landsat, VIIRS, Sentinel 2, MODIS, GPM, and SMAP to answer complex environmental problems in land use, water resource, agriculture, and public health.
He is an author and instructor teaching over 10,000 students online. He has MSc in GIS and Remote Sensing and a PhD in Geospatial Science. He teaches online courses in various themes including Remote Sensing, GIS, Data Science, Machine Learning and Web Mapping.