ML for Remote Sensing Data Course

ML for Remote Sensing Data Course

Course Description

This course provides an overview of remote sensing data analysis. Remote sensing data is already being used to address development issues, i.e., revealing changes in soil quality or water availability, informing agricultural interventions, and even measuring poverty. In this course, we will cover basic principles of satellite imagery most popular techniques to work with it. Also, we will cover the application ranges of machine learning and computer vision on remote sensing data. As well as to get familiar with the theory of remote sensing, we will get hands-on experience in building computer vision models on satellite imagery.

Course tools

  • Python, Tensorflow, Keras, Numpy, QGIS


  • Python
  • Machine Learning basics, including deep learning frameworks (like Keras, PyTorch)
Level of complexity of course Intermediate


Michael Yushchuk Data Science Team Lead at Quantum, for the last 3 years. Michael has experience in many areas of Machine Learning and in-depth expertise in Computer Vision and NLP. The main domains are Agrotech, Healthcare, and Sales. Also, Michael has 6 years of experience in research, including 4 years in Computer Vision. He has several scientific publications and patents. While working at Quantum, he cooperated with Ukraine’s environmental organization. The team led by Michael automates tasks such as monitoring deforestation and the state of forest fields, detecting soil erosion, and so on.    

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