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Monitoring Changes in Surface Water Using Satellite Image Data

  • Course Code: Data Science - Monitoring Changes in Surface Water Using Satellite Image Data
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for those who wants to use the Google Collaboratory (“Colab”) coding environment to access free GPU computer resources and speed up your training times.

Course Snapshot 

  • Duration: 2 days 
  • 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. 
  • Targeted Audience: This course is geared for those who wants to use the Google Collaboratory (“Colab”) coding environment to access free GPU computer resources and speed up your training times. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In this, 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. 

Working in a hands-on learning environment, led by our Monitoring Changes in Surface Water Using Satellite Image Data expert instructor, students will learn about and explore: 

  • 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) 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Getting started 
  • Data Acquisition and pre processing 
  • Enhancing and segmenting images 
  • Model training and evaluation 
  • Model optimization 
  • Reporting to unesco 

Audience & Pre-Requisites 

Pre-Requisites:  Students should have familiar with  

TOOLS 

  • Basic Jupyter 
  • Intermediate NumPy 
  • Intermediate Matplotlib 
  • Basic SciPy 
  • Basic pandas 

TECHNIQUES 

  • Intermediate Python package installation using conda and pip 
  • Basics of neural networks or multi-layer perceptrons 
  • Basic concepts in using digital imagery for environmental monitoring 

Course Agenda / Topics 

  1. Getting started 
  1. Data Acquisition and pre processing 
  1. Enhancing and segmenting images 
  1. Model training and evaluation 
  1. Model optimization 
  1. Reporting to unesco  
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