ENEO: A Public, Interactive Decision-Support Platform for Bridging the Near-Earth Object Impact Risk Perception Gap

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Authors: Alexandros Notas, Dimitrios Kaliampakos
Cited by: 0

Abstract

Near-Earth Object impacts pose a low-probability, high-consequence natural hazard. Despite detection science advancing significantly in recent years, a critical gap persists in communicating and perceiving the actual risks. This paper introduces ENEO (Evaluating Near-Earth Object impacts), a web-based simulation tool developed to address that exact gap and support preliminary decision-making. ENEO makes impact science accessible by integrating established physics and vulnerability models into an interactive, rapid and easy-to-use web platform, which combines these models with global population and economic datasets to make first-order estimations of casualties and economic loss. ENEO’s rapid assessment of hazard enables comparisons of the same celestial body across different geographic regions in seconds and supports rapid decision-making before, during, and after an event, supporting the coordination of warnings, prioritization of evacuations, and targeted allocation of resources. Validation against standard models shows that ENEO provides reliable physics and casualties’ estimation results, proving that the model can provide scientifically consistent assessments of Near-Earth Object impact consequences. Further study shows that ENEO can be used to understand the spatial importance of the impact location and population density effect on human vulnerability, the effect of entry parameters in the impact’s total risk and the relative importance of the phenomena associated with such an event. By visualizing these complex physical interactions, ENEO empowers risk perception to the general public, enhances risk literacy, and contributes to Disaster Risk Reduction.

Fields of Study & Subjects

Space Science and Extraterrestrial Life Astro and Planetary Science Space Satellite Systems and Control

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
3 January 2026
Document type
Preprint