Critical infrastructure, such as transport networks, energy facilities, and urban installations, is increasingly vulnerable to natural hazards and climate change. Remote sensing technologies, namely satellite imagery, offer solutions for monitoring, evaluating, and enhancing the resilience of these vital assets. This paper explores how applications based on synthetic aperture radar (SAR) and optical satellite imagery contribute to the protection of critical infrastructure by enabling near real-time monitoring and early detection of natural hazards for actionable insights across various European critical infrastructure sectors. Case studies demonstrate the integration of remote sensing data into geographic information systems (GISs) for promoting situational awareness, risk assessment, and predictive modeling of natural disasters. These include floods, landslides, wildfires, and earthquakes. Accordingly, this study underlines the role of remote sensing in supporting long-term infrastructure planning and climate adaptation strategies. The presented work supports the goals of the European Union (EU-HORIZON)-sponsored ATLANTIS project, which focuses on strengthening the resilience of critical EU infrastructures by providing authorities and civil protection services with effective tools for managing natural hazards.

The United Nations (UN) framework defines indicator 13.1.1 as the number of deaths, missing persons, and directly affected individuals due to disasters per 100,000 population. This indicator is associated with target 13.1, which calls for urgent actions against climate-related hazards and natural disasters in all countries. However, there is a lack of official data providers and well-established methodologies for assessing the resilience of populated areas to natural disasters. Earth observation (EO), geospatial technologies, and local data may support the estimation of this indicator and, as such, enhance the resilience of specific communities against hazards. Thus, the present study aims to enhance the capacity to monitor Sustainable Development Goals (SDGs) using the abovementioned technologies. In this context, a methodology that integrates ecoregion-specific model training and flood potential related geospatial datasets has been developed to estimate the number of houses affected by floods. This methodology relies on disaster-related databases, such as the UN’s DesInventar, and flood- and exposure-related data, including precipitation and soil moisture products combined with hydro-modelling based on digital elevation models, infrastructure datasets, and population products. By integrating these data sources, different machine learning regression models were trained and stratified by ecoregions to predict the number of affected houses and, as such, provide a more comprehensive understanding of community resilience to floods in the Sahel region. This effort is particularly crucial as the frequency and intensity of floods significantly increase in many areas due to climate change.