Important Dates (subject to potential extensions):

• Paper submission deadline:  30th April, 2025 9th May, 2025

• Paper acceptance notification: 12th May, 2025 22nd May, 2025

• Final paper publication files to be received by:  9th June, 2025

• Session date: TBD

Call for Papers:

The rapid advancements in Artificial Intelligence (AI) and remote sensing technologies have profoundly revolutionized how we observe and preserve our planet. Earth Observation (EO) instruments - including multi-(hyper)spectral optical satellites, radar systems, LiDAR sensors, as well as ground-based and in situ monitoring devices such as meteorological stations, seismic sensors, and IoT-enabled environmental monitoring systems - are generating vast and continuously expanding volumes of data. 

Recent machine learning workflows and deep learning models, provide researchers the capacity to process, monitor, and analyse Earth's dynamic phenomena. These innovative techniques support the extraction of meaningful insights from these high-dimensional and heterogeneous datasets with exceptional precision and granularity. A valuable advancement in EO data analysis is evident in its enhanced capability to identify complex spatio-temporal patterns, detect subtle anomalies, and recognize emerging trends with greater accuracy than conventional analytical methods.

This powerful synergy of AI and remote sensing plays a pivotal role in addressing some of the most crucial global challenges. In the context of climate change, AI-driven analysis of EO data enables the early detection of environmental anomalies, thereby supporting proactive strategies for climate mitigation and adaptation. Similarly, real-time and near-real-time monitoring capabilities are proving instrumental in the rapid identification and management of natural disasters—such as floods, wildfires, and earthquakes—ultimately contributing to risk reduction, disaster resilience, and community protection. Furthermore, the continuous observation of critical infrastructure through AI-enhanced remote sensing techniques is essential for predictive maintenance, early fault detection, and ensuring the resilience of vital systems.

Several emerging methodologies are gaining traction in this domain, including deep learning-based feature extraction, domain adaptation, semi-supervised learning, time series analysis, active learning, explainable AI, uncertainty quantification, and interactive model development and visualization. Despite the increasing adoption of AI techniques and the development of domain-specific algorithms, a significant gap remains in fostering interdisciplinary collaboration between domain experts and AI researchers. Addressing this challenge is crucial to enhancing the interpretability, robustness, and real-world applicability of AI-driven EO systems.

This invited session aims to bring together researchers, practitioners, and industry experts to discuss cutting-edge methodologies for environmental monitoring through AI-driven approaches. We welcome contributions addressing challenges and innovations in EO, particularly those leveraging satellite imagery, weather data collection from space and ground, and computational techniques for sustainability.

We invite original research and case studies related (but not limited) to the following topics:

• Satellite image analysis for environmental monitoring and disaster management

• Modelling and simulation of natural phenomena

• Geospatial data fusion, analytics, and knowledge discovery in Earth Observation

• Explainable and trustworthy AI in climate and Earth Sciences

• Cyber-Physical systems and IoT for real-time Earth Observation

• Edge and On-Board computing for scalable Earth Observation

• Digital traceability and transparent environmental governance

• AI-Driven solutions for sustainable development goals

Contacts

• Stefano Marrone: stefano.marrone@unicampania.it 

• Maria Stella de Biase: mariastella.debiase@unicampania.it 

• Gaetano Settembre: gaetano.settembre@uniba.it