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    Python for Spatial Data Analysis with Earth Engine and QGIS

    Harness the power of big spatial data with Earth Engine Python API and QGIS

    Python for Spatial Data Analysis with Earth Engine and QGIS

    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

    Requirements
    Basic understanding of GIS and Remote Sensing
    Download and install QGIS
    Access to the Google Earth Engine API

    Description
    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 course to Python for Spatial Data Analysis with Earth Engine and QGIS.

    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 plugin. Then, you will have access to satellite data using the 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 Python for Spatial Data Analysis with Earth Engine and QGIS 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 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 QGIS Earth Engine Plugin

    Load Landsat Satellite Data

    Cloud Masking Algorithm

    Calculate NDVI

    Access Sentinel, Landsat, MODIS, CHIRPS, and VIIRS data

    Export images and videos

    Process image collections

    CART classification

    Clustering analysis

    Linear regression

    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 script 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.

    Best,

    Dr. Alemayehu Midekisa

    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

    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
    Genre: eLearning | Language: English | Duration: 28 lectures (3h 17m) | Size: 1.37 GB

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