Talks

Next-Generation Supercomputing: Europe’s Ecosystem for AI in the Exascale Era

Supercomputing combines thousands of interconnected CPU- and GPU-based nodes with large-scale memory, high-speed networks, and specialized storage, acting as a scientific instrument that enables massive simulations, data-intensive analysis, and AI workloads to address complex challenges in physics, chemistry, medicine, energy, climate change and Earth observation.

Talks

Next-Generation Supercomputing: Europe’s Ecosystem for AI in the Exascale Era

Previous talks

Quantum Computing for Earth Observation: Searching for Quantum Advantage

Erasmus+ BIP Machine Learning for Earth Observation and Data Fusion

Next-Generation Supercomputing In Europe: A Community in Action Advancing Hardware, Software, And Application Performance for the Exascale Era

Advances in Geospatial Foundation Models for Earth Observation

Reverse Quantum Annealing for Hybrid Quantum-Classical Satellite Mission Planning

IEEE GRSS HDCRS School 2024

Challenges and Opportunities in the Adoption of High-performance Computing for Earth Observation Applications in the Exascale Era

Remote Sensing for Climate Change Studies in the Arctic

Innovative Computing Technologies for Earth Observation

Seismic Imaging with Remote Sensing for Energy Applications

Towards AI Models based on Quantum Classical Hybrid Computing in Modular Supercomputing Architectures

Hybrid Quantum-classical Workflows In Modular Supercomputing Architectures With The Jülich Unified Infrastructure For Quantum Computing

HDCRS Summer School 2022

Machine Learning For Remote Sensing Applications Powered By Modular Supercomputing Architectures

High-performance, specialized and quantum computing for remote sensing. A new GRSS working group

Quantum Support Vector Machine Algorithms

Deep Learning for Earth Observation powered by High Performance Computing

Practice and Experience in using Parallel and Scalable Machine Learning in Remote Sensing from High Performance Computing over Cloud to Quantum Computing

Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification

Enhancing Large Batch Size Training of Deep Models for Remote Sensing Applications

High-performance and Disruptive Computing in Remote Sensing (HDCRS): a new working group of the GRSS Earth Science Informatics Technical Committee

Distributed Deep Learning with High Performance Computing for Large-Scale Remote Sensing Data

Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer

Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning

Scaling Up a Multispectral Resnet-50 to 128 GPUs

High Performance Computing Systems for Big Earth Observation Data Mining and Applications

Remote sensing data analytics with the udocker container tool using multi-GPU deep learning systems

Quantum Computing for Earth Observation:Searching for Quantum Advantage