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Subject:
From:
Luciano Caroprese <[log in to unmask]>
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Date:
Fri, 8 Sep 2023 01:32:45 -0400
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CALL FOR PAPERS

Online Special Session 'Machine Learning for Earth Observation (ML4EO)' @ ICMLA 2023

Online Special Session at the 22nd IEEE International Conference on Machine Learning and Applications
(IEEE ICMLA-2023)
https://www.icmla-conference.org/icmla23/vss-15.html

Format: Online Special Session

----------------------------------------
SPECIAL SESSION DATE:  December 15-17, 2023

LOCATION: Hyatt Regency Jacksonville Riverfront, Florida

WEBSITE:  https://www.icmla-conference.org/icmla23/
----------------------------------------

SCOPE
--------------
Machine Learning plays an emerging role in analyzing big data, captured by IOT devices, in different ecosystems,
from structural health monitoring (SHM) to guarantee the integrity and safety of structures tracking the progression
of damage, and predicting performance degradation, to the analysis of remotely sensed data for Earth Observation (EO)
in order to prevent risks and catastrophes, and monitor atmospheric data, climate change and the health status of the earth.
Despite the ease of data retrieval, the analysis of the big datasets collected in Earth Information Systems (EIS) -
also by means of IOT devices - remains a significant barrier for scientists and analysts. While traditional analysis
provides some insights into the data, the complexity, scale, and multidisciplinary nature of the data needs advanced
and intelligent solutions.

Recently, the research community has achieved significant advances in artificial intelligence (AI). In particular, deep neural networks (DNNs) and massive datasets have facilitated progress in AI tasks such as image classification, object detection, scene recognition, semantic segmentation, and time series analysis.
The purpose of this special session is to bring together researchers, developers, and practitioners in machine learning and data science to address the challenges of machine learning for analyzing big data in Earth Information systems.

The special session solicits empirical, experimental, methodological, and theoretical research reporting original results on topics in the fields of machine learning applied to Earth observation. The purpose of the special session is to share the latest research and developments in AI techniques for forecasting new solutions and applications to real-life situations.
The special session will be of interest to researchers, engineers, and industry professionals and  will provide an opportunity for participants to learn from one another, share best practices, and collaborate on future research and development in this important field

Topics of interest include, but are not limited to:
•        Structural Health Monitoring
•        Machine Learning Models for EO: Interpretability and Explainability
•        Early disease diagnosis and treatment prediction
•        Modeling the health status and well-being of the earth
•        Clinical decision support in disease diagnosis and treatment
•        Analysis and interpretation of signals and  images for EO
•        Application of deep learning methods to earth data
•        Spatio-temporal prediction of damage and degradation in EO
•        Real-time surveillance and early detection of emerging earth situations
•        Blockchain for EO
•        Emerging challenges in EO
•        Social media analysis and EO
•        Machine Learning and Data Fusion for EO
•        Novel methods and frameworks for mining and integrating big earth data
•        Semantics and interoperability for earth data
•        Data privacy and security for Earth data
•        Clinical natural language processing and text mining
•        Predictive modelling for diagnosis and treatment in EO
•        Fake news in earth catastrophes
•        Data analytics for pervasive computing for earth care
•        IOT for EO


Format: Online Special Session



PROGRAM CHAIRS
--------------
Luciano Caroprese, University of Chieti-Pescara, [log in to unmask]
Maria Giovanna Masciotta, University of Chieti-Pescara, [log in to unmask]
Sergio Montelpare, University of Chieti-Pescara, [log in to unmask]
Francesco Potenza, University of Chieti-Pescara, [log in to unmask]
Ester Zumpano, University of Calabria, [log in to unmask]



PAPER SUBMISSION FORMATS
--------------
Papers submitted for review should conform to IEEE specifications
with a maximum length of 6 pages including references and any other additional
material (up to 2 pages could be added with extra charge, please refer to
registration page for details). Manuscript templates can be downloaded from
IEEE website
(http://www.ieee.org/conferences_events/conferences/publishing/templates.html).
 All submissions must be anonymized and may not contain any information
with the intention or consequence of violating the double-blind reviewing
policy, including (but not limited to) citing previous works of the authors
or sharing links in a way that can infer any author’s identity or institution,
actions
that reveal the identities of the authors to potential reviewers.

SUBMISSION:
-------------------
Papers must be submitted via the CMT System. Please follow the
link (https://cmt3.research.microsoft.com/ICMLA2023/)  and
Select the track: Virtual Special
Session 15: Machine Learning for Earth Observation.
All accepted papers must be presented by one of the authors,
who must be registered.

IMPORTANT DATES
--------------
- Submission deadline: September 21, 2023 
- Acceptance/Rejection Notification: September 25, 2023
- Camera-ready & Pre-Registration: October 5, 2023


PUBLICATION
--------------
Accepted papers will be published in the IEEE ICMLA 2023
conference proceedings (published by IEEE). A selected number
of accepted papers will be invited for possible inclusion, in
an expanded and revised form,
in some journal special issues.
---------------


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