Advancements in EO-Based Methodologies and Applications to Tackle Civil Security Scenarios

In the domain of civil security, the evolving geopolitical landscape and growing exposure to hybrid, environmental and humanitarian crises are generating increasingly complex scenarios. Traditional domains such as critical infrastructure monitoring, crisis response and maritime awareness are now intersecting with emerging dimensions — including climate, energy and health security — that demand integrated and adaptive solutions. Addressing these challenges requires multidisciplinary efforts to translate situational awareness into effective operational and policy responses.

Earth Observation (EO) data has become a cornerstone for evidence-based decision-making in this context. The exponential increase in EO missions, coupled with contributions from both institutional and commercial actors, has radically expanded data availability. However, this abundance also exposes critical gaps: the difficulty of integrating heterogeneous data streams, ensuring trust and interoperability, and turning large-scale data into actionable intelligence in time-sensitive security contexts.

This Research Topic aims to advance innovative EO-based approaches that specifically address these gaps and strengthen civil security preparedness and resilience. We invite contributions that explore how AI/ML, Big Data Analytics and Data Fusion can enhance the analytical and operational value of EO information for civil security decision-making. We particularly welcome work demonstrating novel methods and applications that improve our capacity to monitor, anticipate and respond to security-relevant events.

Contributions may include (but are not limited to):
• Integration of Big Data Analytics with heterogenous data in security response systems
• AI and Machine Learning for enhanced EO data analysis
• Advanced image processing (e.g. multimodal and multitemporal) for civil security applications
• Innovative techniques and approaches for EO-based security applications (e.g. Semantic Knowledge Graphs and Digital Twins for Security applications)

By collecting these studies, the Research Topic aims to stimulate cross-sector collaboration between EO scientists, security practitioners and policymakers. Its expected impact lies in informing policy frameworks, operational practices, and the future design of EO missions dedicated to security and resilience.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: remote sensing, civil security, earth observation, big data, machine learning, artificial intelligence, AI, data fusion, image processing, monitoring

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.