Talks

Erasmus+ BIP Machine Learning for Earth Observation and Data Fusion

The programme aims at training professionals trained in the use of geospatial data coming from satellite for Earth Observation applications, such as the monitoring of the UN Sustainable Development Goals. It is based on a virtual mobility that provides the basic of machine learning techniques applied to satellite data sets and challenges the participants to provide answers to data processing questions in groups of students by different institutions.

Talks

Erasmus+ BIP Machine Learning for Earth Observation and Data Fusion

Previous talks

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