QGIS and Google Earth Engine Python API for Spatial Analysis
What you'll learn
- Students will access and sign up the Google Earth Engine Python API platform
- Download, and install QGIS
- Access satellite data in Earth Engine
- Export geospatial Data
- Access image collections
- Learn to access and analyze various satellite data including, MODIS, Sentinel and Landsat
- Cloud masking of Landsat images
- Visualize time series images
- Extract information from satellite data
- This course has no requirements.
Do you want to access satellite sensors using Earth Engine Python API?
Do you want to learn the QGIS Earth Engine plugin?
Do you want to visualize and analyze satellite data in Python?
Enroll in my new QGIS and Google Earth Engine Python API for Spatial Analysis course.
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 QGIS and Earth Engine plugins. Then, you will have access to satellite data using the Python API.
In this QGIS and Google Earth Engine Python API for Spatial Analysis course, I will help you get up and running on the Earth Engine Python API and QGIS. 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 QGIS Earth Engine Plugin
Load Landsat Satellite Data
Cloud Masking Algorithm
Access Sentinel, Landsat, MODIS, CHIRPS, and VIIRS data
Export images and videos
Process image collections
Global Land Cover Products (NLCD, and MODIS Land Cover)
One of the common problems with learning image processing is the high cost of software. In this course, I entirely use the Google Earth Engine Python API and QGIS open-source tools. All sample data and scripts will be provided to you as an added bonus throughout the course.
Jump in right now to enroll. To get started click the enroll button.
Who this course is for:
- This course is meant for professionals who want to harness the power Google Earth Engine Python API and QGIS
- People who want to understand various satellite image processing techniques using Python
- Anyone who wants to learn accessing visualizing and extracting information from satellites
- People who are working with satellite remote sensing data such as Landsat, MODIS, and Sentinel-2
- Anyone who wants to apply for GIS or Remote Sensing Specialist job position
I am a geospatial data scientist with 15-plus years of experience. I am a former NASA Earth and Space Science fellow. My research interests include remote sensing, big data, and environmental change. More specifically, I am interested in applying big geospatial data, cloud computing, and machine learning to solve complex environmental problems, especially land cover change, climate change, water resources, agriculture, and public health.