Special Session 5:
Machine Learning for Earth
Observation (ML4EO)
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 remote 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, 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 the 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.
Scope and topics:
Topics relevant to this session 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
Chairs: Luciano Caroprese, Maria Giovanna Masciotta,Sergio Montelpare, Francesco Potenza, Ester Zumpano, Jerry Bonnell
Bio:
Luciano Caroprese is currently a Researcher at the Department of Engineering and Geology (InGeo) of the University "G. d'Annunzio "of Chieti and Pescara. He received a Ph.D. in Systems Engineering and Computer Science from the University of Calabria in 2008. During his research activity, he worked on data integration techniques for P2P systems, view updating strategies for deductive databases, deep learning models for recommendation systems, and online machine-learning techniques for data streams. He is also currently working on deep learning models for audio stream processing. Email: luciano.caroprese@unich.it
Maria Giovanna Masciotta is a fixed-term Researcher at the Department of Engineering and Geology (InGeo) of the University "G. d'Annunzio "of Chieti and Pescara. She obtained a dual-degree PhD in Civil Engineering in 2015 and spent the initial part of her career at the Institute for Sustainability and Innovation in Structural Engineering (ISISE), i.e. the largest research unit in the field of Structural Engineering in Portugal. She has been actively working in the fields of Structural Health Monitoring, damage identification, maintenance and preventive conservation of historical masonry structures for the past ten years. Her scientific research interests also embrace the areas of optimal sensor placement for bridge monitoring, advanced modelling strategies and large-scale seismic vulnerability assessment of historic centres.Email: mariagiovanna.masciotta@unich.it
Sergio Montelpare is full professor of Applied Thermodynamics and Heat Transfer at the University "G. d'Annunzio" of Chieti-Pescara. He graduated in Mechanical Engineering, "summa cum laude", at the "Polytechnic University of Marche" (Ancona, Italy), where he also obtained his Ph.D. degree in Applied Thermodynamics and Heat Transfer (XIVth cycle) in 2001. His research activities are focused on: experimental and numerical thermo-fluid dynamics; thermo-physics of buildings; environmental acoustics; energy production from renewable sources. He is a member of the editorial board of the "Applied Sciences" Journal, guest editor of special issues of the Applied Sciences journal, reviewer for numerous indexed journals and authored more than 87 publications among international journal papers, conference proceedings and book chapters. From 2017 to 2020 he was a member of the Ph.D. Program Committee in "Earth systems and built environments" and since 2020 of the Ph.D. Program Committee in "Science and Technology for Sustainable Development". From 2018 he is a member of the Italian Network for Sustainable Universities (RUS). From 2019 he is President of the Bachelor's degree course in "Building Engineering" and from 2021 of the Master's degree in "Building Engineering". Email: sergio.montelpare@unich.it
Francesco Potenza is currently Associate Professor of Structural Engineering at the Department of Engineering and Geology (INGEO) of the University "G. d'Annunzio" of Chieti-Pescara. He received a Ph.D. in in Mathematical Modelling in Engineering (University of Aquila). Main scientific topics are related to structural dynamics and control (passive, active and semi-active), damage and modal identification, structural health monitoring, dynamics of cable structures. Email: francesco.potenza@unich.it
Ester Zumpano is an associate professor of Computer Engineering at the Department of Computer Engineering, Modeling, Electronics and Systems Engineering, University of Calabria (DIMES). She received a Phd in Computer Science from the University of Calabria in 2003. Her areas of research include health information systems, data integration, logic programming, view updating, distributed systems, artificial Intelligence, database management. She is member of the Scientific Board of the Ph.D. Course in Information and Communication Technologies, University of Calabria, Italy. She has many international collaborations. She is a founding member of the ITACA S.r.l. spin-off. She is member of the Steering Committee of the European Conference on Advances in Databases and Information Systems (ADBIS) Conference (from 2007 until now). Member of the Editorial Board of the Journal" Intelligent Information Systems"; Member of the Editor Board of the Journal "Big Data and Cognitive Computing". Email: e.zumpano@dimes.unical.it
Jerry Bonnell is a Post Doctoral Associate at the Frost Institute for Data Science and Computing at the University of Miami. He received a Ph.D. in Computer Science from the University of Miami in 2023. Main scientific topics are related to machine learning methods for bioinformatics and personalized medicine, machine learning models for recommendation systems, natural language processing for cultural analytics, and digital humanities.
Technical Committee
- Luciano Caroprese, University of Chieti-Pescara, Italy
- Maria Giovanna Masciotta, University of Chieti-Pescara, Italy
- Elio Masciari, Universita degli Studi di Napoli Federico II, Italy
- Sergio Montelpare, University of Chieti-Pescara, Italy
- Francesco Potenza, University of Chieti-Pescara, Italy
- Shahaboddin Shamshirband, Ton DucThang University, Viet Nam
- Domenico Ursino, Universita Politecnica delle Marche, Italy
- Luca Virgili, Universita Politecnica delle Marche, Italy
- Eugenio Vocaturo, University of Calabria, Italy
- Ester Zumpano, University of Calabria, Italy
- Jerry Bonnell, University of Miami
Paper Submission Instructions
All papers will be double-blind reviewed and must present original work.
- CMT Submission Site
- Select the track: Special Session 3: Machine Learning for Predictive Models in Engineering Applications
Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:
Keydates
- Submission due date: September 9, 2024
- Notification of Acceptance: September 25, 2024
- Camera Ready Papers: October 5, 2024
- Pre-registration: October 15, 2024
- Conference: December 18-20, 2024
Registration
In order for your paper to be presented in the virtual session and published in the proceedings you must register to the conference.
Paper Presentation Instructions
The papers submitted to this track will be presented in person as part of the conference. There is no virtual presentation for this session.
ICMLA'24