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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Basic Jupyter, SciPy, pandas, and neural networks or multi-layer perceptions, digital imagery for environmental monitory skills, and intermediate NumPy, Matplotlib, & Python package installation skills

Who should attend & recommended skills

  • This course is geared for those who want to use the Google Collaboratory (&;) coding environment to access free GPU computer resources and speed up your training times.
  • Skill-level: Foundation-level Monitoring Changes in Surface Water Using Satellite Image Data skills for Intermediate skilled team members. This is not a basic class.
  • Jupyter: Basic (1-2 years’ experience)
  • SciPy: Basic (1-2 years’ experience)
  • pandas: Basic (1-2 years’ experience)
  • Neural networks or multi-layer perceptrons: Basic (1-2 years’ experience)
  • Using digital imagery for environmental monitoring: Basic concepts (1-2 years’ experience)
  • NumPy: Intermediate (3-5 years’ experience)
  • Matplotlib: Intermediate (3-5 years’ experience)
  • Python package installation using conda and pip: Intermediate (3-5 years’ experience)

About this course

In this course, you’ll fill the shoes of a data scientist at UNESCO (United Nations Educational, Scientific and Cultural Organization). Your job involves assessing long-term changes to freshwater deposits, one of humanity’s most important resources. Recently, two European Space Agency satellites have given you a massive amount of new data in the form of satellite imagery. Your task is to build a deep learning algorithm that can process this data and automatically detect water pixels in the imagery of a region. To accomplish this, you will design, implement, and evaluate a convolutional neural network model for image pixel classification, or image segmentation. Your challenges will include compiling your data, training your model, evaluating its performance, and providing a summary of your findings to your superiors. Throughout, you’ll use the Google Collaboratory (“Colab”) coding environment to access free GPU computer resources and speed up your training times.

Skills acquired & topics covered

  • Accessing cloud data servers to download satellite imagery
  • Manually creating your own ground truth data from imagery
  • Using the VGG-JSON image annotation format
  • Using Graphical Processing Unit (GPU) computation on Google Colab
  • Merging imagery and performing operations on raster datasets
  • Using Keras and TensorFlow for deep learning
  • Evaluating model performance by comparing estimated and observed results
  • Data augmentation for boosting model training
  • Optimizing model performance using experimentation
  • Understanding model performance metrics (such as Dice and Jaccard scores)