QGIS and Google Earth Engine Python API for Spatial Analysis
QGIS and Google Earth Engine Python API for Spatial Analysis, available at $54.99, has an average rating of 4.2, with 29 lectures, based on 99 reviews, and has 595 subscribers.
You will learn about 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 is ideal for individuals who are This course is meant for professionals who want to harness the power Google Earth Engine Python API and QGIS or People who want to understand various satellite image processing techniques using Python or Anyone who wants to learn accessing visualizing and extracting information from satellites or People who are working with satellite remote sensing data such as Landsat, MODIS, and Sentinel-2 or Anyone who wants to apply for GIS or Remote Sensing Specialist job position It is particularly useful for This course is meant for professionals who want to harness the power Google Earth Engine Python API and QGIS or People who want to understand various satellite image processing techniques using Python or Anyone who wants to learn accessing visualizing and extracting information from satellites or People who are working with satellite remote sensing data such as Landsat, MODIS, and Sentinel-2 or Anyone who wants to apply for GIS or Remote Sensing Specialist job position.
Enroll now: QGIS and Google Earth Engine Python API for Spatial Analysis
Summary
Title: QGIS and Google Earth Engine Python API for Spatial Analysis
Price: $54.99
Average Rating: 4.2
Number of Lectures: 29
Number of Published Lectures: 29
Number of Curriculum Items: 29
Number of Published Curriculum Objects: 29
Original Price: $49.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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:
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Introduction to Earth Engine Python API
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Install the QGIS Earth Engine Plugin
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Load Landsat Satellite Data
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Cloud Masking Algorithm
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Calculate NDVI
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Access Sentinel, Landsat, MODIS, CHIRPS, and VIIRS data
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Export images and videos
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Process image collections
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CART classification
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Clustering analysis
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Linear regression
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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.
Course Curriculum
Chapter 1: Introduction to Earth Engine Python API
Lecture 1: Welcome
Lecture 2: Sign Up on Earth Engine
Lecture 3: Install Earth Engine Plugin in QGIS
Lecture 4: Load Landsat Images
Lecture 5: Calculate NDVI
Lecture 6: Map Image Collection
Lecture 7: Landsat Cloud Mask
Chapter 2: Geospatial Data Visualization
Lecture 1: Earth Observation Satellites
Lecture 2: Landsat Visualization
Lecture 3: MODIS Land Cover Visualization
Lecture 4: NLCD Land Cover Visualization
Lecture 5: NDVI Visualization
Lecture 6: NDWI Visualization
Lecture 7: Terrain Visualization
Chapter 3: Access Raster Data Using Earth Engine Python API
Lecture 1: Sentinel
Lecture 2: CHIRPS
Lecture 3: VIIRS Nighttime Light
Lecture 4: MODIS NDVI
Chapter 4: Images in Earth Engine Python API
Lecture 1: Download
Lecture 2: Clipping
Lecture 3: Image Metadata
Chapter 5: Machine Learning in Earth Engine Python API
Lecture 1: Clustering
Lecture 2: CART Classification
Chapter 6: Advanced Algorithms
Lecture 1: Spectral Unmixing
Lecture 2: Linear Regression
Lecture 3: Object-based Detection
Lecture 4: SMAP Soil Moisture
Chapter 7: Final Project
Lecture 1: Final Project
Chapter 8: Bonus Lectures
Lecture 1: Bonus
Instructors
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Dr. Alemayehu Midekisa
Geospatial Data Scientist
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 6 votes
- 3 stars: 23 votes
- 4 stars: 38 votes
- 5 stars: 29 votes
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