Quantum Machine Learning in Remote Sensing | HDCRS School Podcast Ep. 3

🌍 Quantum Machine Learning in Remote Sensing | HDCRS School Podcast Ep. 3 🌍

How can Quantum Machine Learning (QML) contribute to solving current challenges in Remote Sensing (RS) and Earth Observation (EO)? In this third and final episode of the HDCRS School Podcast, we delve into the emerging field of QML and its potential to tackle complex geospatial data analysis problems in new ways.

🎙️ Featured Experts (in order of appearance):

🔹 Dr. Artur Miroszewski – Postdoctoral researcher at Jagiellonian University (Poland). He obtained his PhD from the National Centre for Nuclear Research in Warsaw, with a focus on quantum technologies.

🔹 Dr. Gabriele Cavallaro – Adjunct Associate Professor at the University of Iceland and Co-chair of the IEEE GRSS Earth Science Informatics (ESI) Technical Committee.

🛰️ Topics covered in this episode: ✔️ What is Quantum Machine Learning (QML)? ✔️ Benefits and limitations of QML in remote sensing applications ✔️ Real-world examples of QML in Earth observation ✔️ Future perspectives on quantum technologies in geospatial science

🔗 More resources & contacts: 📖 Learn more about HDCRS Working Group: https://www.hdc-rs.com 💡 Follow IEEE GRSS: https://www.grss-ieee.org 📩 For inquiries: hdcrs.school@gmail.com