Coluzzi et al. (2025) Towards a risk-informed land system approach in the age of artificial intelligence and analysis-ready satellite data

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Geomatics Natural Hazards and Risk
• Year: 2025
• Date: 2025-12-21
• Authors: Rosa Coluzzi, Maria Lanfredi, Vito Imbrenda
• DOI: 10.1080/19475705.2025.2599487

Research Groups

[Not specified in the provided text, as this is a survey paper.]

Short Summary

This paper surveys currently available satellite-derived tools and analysis-ready Earth Observation (EO) products, highlighting the growing role of Artificial Intelligence (AI) in transforming complex EO data into accessible and actionable knowledge for risk-informed management within Land System Science (LSS).

Objective

• To present a brief survey of satellite-derived tools currently available to support risk-informed management and planning strategies within Land System Science (LSS).
• To explore ready-to-use Earth Observation (EO) products that enable early detection and assessment of land system vulnerabilities.
• To highlight the growing role of Artificial Intelligence (AI) in transforming EO datasets into predictive, accessible, and decision-relevant information.
• To discuss expected future developments for sustainable risk governance in the global change era.

Study Configuration

• Spatial Scale: Conceptual; covers various scales relevant to Land System Science, from local to global.
• Temporal Scale: Conceptual; focuses on current tools and future developments for continuous monitoring, early detection, and risk anticipation in the global change era.

Methodology and Data

• Models used: Not applicable (survey paper); discusses the integration of Artificial Intelligence (AI) into Earth Observation (EO) workflows.
• Data sources: Satellite imagery; Earth Observation (EO) data (as discussed in the survey).

Main Results

• The potential of satellite imagery within Land System Science (LSS) for risk identification and management remains underexploited due to the technical complexity of raw Earth Observation (EO) data.
• Analysis-ready EO products and the integration of Artificial Intelligence (AI) are crucial for enhancing the ability to detect, anticipate, and respond to land-related risks by making data accessible and actionable for non-specialists.
• Ready-to-use EO products enable early detection and assessment of land system vulnerabilities.
• AI plays a growing role in transforming EO datasets into predictive, accessible, and decision-relevant information, facilitating risk-informed management and planning.

Contributions

• Provides a comprehensive survey of currently available satellite-derived tools and ready-to-use Earth Observation (EO) products for risk-informed management in Land System Science (LSS).
• Identifies the technical complexity of raw EO data as a key barrier and proposes solutions through analysis-ready products and AI integration.
• Emphasizes the critical role of Artificial Intelligence (AI) in making complex EO data accessible and actionable for non-specialists, thereby enhancing decision-making.
• Discusses future developments for sustainable risk governance in the context of global change.

Funding

[Not specified in the provided text.]

Citation

@article{Coluzzi2025Towards,
  author = {Coluzzi, Rosa and Lanfredi, Maria and Imbrenda, Vito},
  title = {Towards a risk-informed land system approach in the age of artificial intelligence and analysis-ready satellite data},
  journal = {Geomatics Natural Hazards and Risk},
  year = {2025},
  doi = {10.1080/19475705.2025.2599487},
  url = {https://doi.org/10.1080/19475705.2025.2599487}
}