GEE Advanced Python Integration

Details

Length: 2 days
Time: Office hours in your timezone
Location: Online – US, EU and SE Asian timezones
Or contact NGIS on the form at the bottom of the page to request a corporate course.

The Earth Engine Advanced Python Integration course extends skills developed within the Foundational and Advanced courses into a python programming environment.

The course is designed to extend beyond a strictly Earth Engine processing environment through an introduction of the Earth Engine Python API. The course is delivered using Google Vertex AI – a cloud-based python notebook environment in a similar vein to Jupyter or Colab notebooks. The course begins with an exploration of the notebook environment and of Python, before moving into familiar analyses executed using the Python API in place of the more familiar JavaScript API. The course expands to explore different ways of moving data both out of and into the Earth Engine environment for additional processing. These environments include local Python objects, local storage, cloud storage, and BigQuery. Data processing is explored with external Python libraries including Numpy, Pandas, Scikit-image, and Scikit-learn. The course also explores the development and implementation of a Python-based processing pipeline. Processing pipelines are necessary for scaling large-scale analyses that may otherwise exhaust the memory limitations of Earth Engine.

NGIS are able to offer group training sessions for a team of six or more on an agreed date online only. Please send an email to training@ngis.com.au to start this discussion.

Google Earth Engine Fundamentals or equivalent skills

Google Vertex AI
Python
Earth Engine Python API
Geemap
Exporting/Importing Data (local objects, local files, cloud storage, BigQuery)
Analysis of Data (Numpy, Pandas, Scikit-image, Scikit-learn, BigQuery)
Processing pipelines

Scikit-learn Image Classification and Processing Pipeline.

  • Image classification using Scikit-learn, including exporting and importing data from EE, data partitioning, and task management of a large-scale analysis.

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