Quantum starts the “ML for Remote Sensing Data” course for Lviv Data Science Summer from the Ukrainian Catholic University.
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 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.
— Python, Tensorflow, Keras, Numpy, QGIS.
— Machine Learning basics, including deep learning frameworks (like Keras, PyTorch)
Level of complexity of course
Michael Yushchuk is the Data Science Team Lead at Quantum with 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 six years of experience in research, including four 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.