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    Spatial Data Analysis with Earth Engine Python API

    Learn machine learning, big data analysis, GIS, remote sensing with Earth Engine Python API and Jupyter Notebook

    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

    Download and Install Anaconda and Jupyter Notebook
    Basic understanding of GIS and Remote Sensing
    Access to the Google Earth Engine API

    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.


    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

    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
    Genre: eLearning | Language: English + .srt | Duration: 23 lectures (2h 15m) | Size: 951 MB

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