1st Workshop on Complex Data Challenges in Earth Observation (CDCEO)

Workshop Website https://www.iarai.ac.at/events/workshop-on-complex-data-challenges-in-earth-observation/

Workshop Organisers

  • Aleksandra Gruca
    Silesian University of Technology, Poland
  • Pedro Herruzo
    Institute of Advanced Research in Artificial Intelligence, Austria
  • Pilar RĂ­podas
    Spanish Meteorological Agency, Spain
  • Pedram Ghamisi
    Institute of Advanced Research in Artificial Intelligence, Austria; Helmholtz-Zentrum Dresden-Rossendorf, Germany
  • Christian Briese
    Earth Observation Data Centre for Water Resources Monitoring, Austria
  • Andrzej Kucik
    European Space Agency Centre for Earth Observation, Italy
  • Michael Kopp
    Institute of Advanced Research in Artificial Intelligence, Austria; Here Technologies, Switzerland
  • David Kreil
    Institute of Advanced Research in Artificial Intelligence, Austria
  • Sepp Hochreiter
    Institute of Advanced Research in Artificial Intelligence, Austria

Workshop Abstract

High-resolution remote sensing technology for Earth Observation (EO) has radically changed how we monitor the state of our planet around the clock. An effective interpretation of the resulting complex large-scale time series adopts the best machine learning techniques from signal processing, computer vision, pattern recognition, and AI. This workshop brings together leading researchers from academia and industry across these domains.

The ever-growing availability of high-resolution remote sensing data increasingly brings into focus new challenges posed by their heterogeneity and correlation structures. The correlated multi-dimensional non-linear measurements over time reflect dynamic states with complex inter-dependencies.

The workshop, thus, invites both method development and advanced applications in a wide range of related topics, including image and signal processing, gap-filling, data fusion, feature extraction, prediction of spatio-temporal features, and the detection of rules underlying the observed state transitions and causal relationships.

In EO imaging, weather forecasts are of obvious immediate value. Novel insights from identified patterns are critical for a better understanding of our environment. A special session will thus present highlights from a unique multi-sensor weather forecasting competition.

Advances in EO will form the basis for effectively addressing urgent social challenges affected by changes in our environment such as natural catastrophes or climate change.