Spatial Data Analysis in Google Earth Engine Python API

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

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Description

Do you want to access satellite sensors using Earth Engine Python API and Jupyter Notebook?

Do you want to learn spatial data science on the cloud?

Do you want to become a spatial data scientist?


Enroll in my new course Spatial Data Analysis in Google Earth 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.


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 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 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 scripts will be provided to you as an added bonus throughout the course.


Jump in right now and enroll.

What You Will 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

Who Should Attend!

  • 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