************************************************************************
IERS Message No. 495                                   December 04, 2023
************************************************************************


EGU 2024: Data Science and Machine Learning in Geodesy


Dear Colleagues,

We hope this message finds you well. We are reaching out to invite you
to participate in the G1.3 session on "Data Science and Machine Learning
in Geodesy" at the upcoming EGU General Assembly 2024, to be held from
April 14-19 in Vienna, Austria and online.

Abstract submission details: 

Abstract submission link:
https://meetingorganizer.copernicus.org/EGU24/session/49265

Submission deadline:
Wednesday, 10 January 2024, 13:00 CET

Session overview:
G1.3 - Data Science and Machine Learning in Geodesy

Session details:
"This session aims to showcase novel applications of
data science and machine learning methods in geodesy.

In recent years, the exponential growth of geodetic data from various
observation techniques has created challenges and opportunities.
Innovative approaches are required to efficiently handle and harness the
vast amount of geodetic data available nowadays for scientific purposes,
for example when dealing with "big data" from Global Navigation
Satellite System (GNSS) and Interferometric Synthetic Aperture Radar
(InSAR). Likewise, numerical weather models and other environmental
models important for geodesy come with ever-growing resolutions and
dimensions. Strategies and methodologies from the fields of data science
and machine learning have shown great potential not only in this context
but also when applied to more limited data sets to solve complex
non-linear problems in geodesy.

We invite contributions related to various aspects of applying methods
from data science and machine learning (including both shallow and deep
learning techniques) to geodetic problems and data sets. We welcome
investigations related to (but not limited to): more efficient and
automated processing of geodetic data, pattern and anomaly detection in
geodetic time series, images or higher-dimensional data sets, improved
predictions of geodetic parameters, such as Earth orientation or
atmospheric parameters into the future, combination and extraction of
information from multiple inhomogeneous data sets (multi-temporal,
multi-sensor, multi-modal fusion), feature selection, super-sampling of
geodetic data, and improvements of large-scale simulations. We strongly
encourage contributions that address crucial aspects of uncertainty
quantification, interpretability, and explainability of machine learning
outcomes, as well as the integration of physical models into data-driven
frameworks.

By combining the power of artificial intelligence with geodetic science,
we aim to open new horizons in our understanding of Earth's dynamic
geophysical processes. Join us in this session to explore how the fusion
of physics and machine learning promises advantages in generalization,
consistency, and extrapolation, ultimately advancing the frontiers of
geodesy."

We look forward to receiving your valuable contributions and thank you
very much for your attention.

Submit your abstract
https://meetingorganizer.copernicus.org/EGU24/session/49265 to share
your work on applying ML to geodetic problems. We appreciate your
consideration and eagerly await your participation in this exciting
session."

Best regards from session conveners,

Benedikt Soja, Maria Kaselimi, Milad Asgarimehr, Sadegh Modiri and
Alex Sun


************************************************************************
IERS Messages are edited and distributed by the IERS Central Bureau.
If not stated otherwise, the IERS is only the distributor of the message
and is not responsible for its content.
To submit texts for distribution, please write to
<central_bureau@iers.org>.
To subscribe or unsubscribe, please create an IERS account or modify it:
https://www.iers.org/Login/Login/EN/login_node.html
or write to <central_bureau@iers.org>.
Archives: http://www.iers.org/IERS/Messages
************************************************************************