Remote Sensing as a Sentinel for Safeguarding European Critical Infrastructure in the Face of Natural Disasters
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Flood Risk Assessment and Management · Synthetic Aperture Radar (SAR) Applications and Techniques · Infrastructure Resilience and Vulnerability Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
European Union Satellite Centre (SatCen), 28850 Torrejon de Ardoz, Spain
RESALLIENCE c/o SIXENSE Engineering, 22 Rue Lavoisier, 92000 Nanterre, France
Links Foundation, Via Pier Carlo Boggio, 61, 10138 Torino, TO, Italy
Funding
EU-ATLANTIS Horizon
References (27)
Show source-provided references
1. The “Build-Back-Better” Concept for Reconstruction of Critical Infrastructure: A Review10.1016/j.ssci.2022.105932 ↗
2. Rios, E., Iturbe, E., Rego, A., Ferry, N., Tigli, J.-Y., Lavirotte, S., Rocher, G., Nguyen, P., Song, H., and Dautov, R. (September, January 29). The DYNABIC Approach to Resilience of Critical Infrastructures. Proceedings of the The DYNABIC Approach to Resilience of Critical Infrastructures, Benevento, Italy.
3. Sathurshan, M., Saja, A., Thamboo, J., Haraguchi, M., and Navaratnam, S. (2022). Resilience of Critical Infrastructure Systems: A Systematic Literature Review of Measurement Frameworks. Infrastructures, 7.10.3390/infrastructures7050067 ↗
4. European Commission, Joint Research Centre, and Institute for the Protection and the Security of the Citizen (2014). Towards Testing Critical Infrastructure Resilience, Publications Office.
5. Bateman, J. (2012). Proceedings of the Institution of Civil Engineers—Municipal Engineer, ICE Publishing.
6. Sänger, N., Heinzel, C., and Sandholz, S. (2021). Advancing Resilience of Critical Health Infrastructures to Cascading Impacts of Water Supply Outages—Insights from a Systematic Literature Review. Infrastructures, 6.10.3390/infrastructures6120177 ↗
7. Rehak, D., Slivkova, S., Janeckova, H., Stuberova, D., and Hromada, M. (2022). Strengthening Resilience in the Energy Critical Infrastructure: Methodological Overview. Energies, 15.10.3390/en15145276 ↗
8. Badolo, M. (2024). Modeling and Planning Interdependent Critical Urban Infrastructures Resilience to Extreme Events: The Badolo Cires Model. Authorea.10.22541/au.171148942.23175536/v1 ↗
9. Almaleh, A., and Tipper, D. (2021). Risk-Based Criticality Assessment for Smart Critical Infrastructures. Infrastructures, 7.10.3390/infrastructures7010003 ↗
10. Threat Modeling of Cyber-Physical Systems—A Case Study of a Microgrid System10.1016/j.cose.2022.102950 ↗
11. Haces-Garcia, F., Glennie, C.L., and Rifai, H.S. (2022). Sustainability of Network Infrastructure in a Geospatial Resilience Context. Sustainability, 14.10.3390/su141811415 ↗
12. An Approach for Analysis of Critical Infrastructure Vulnerability to Climate Hazards10.1088/1755-1315/1094/1/012004 ↗
13. Liu, W., Shan, M., Zhang, S., Zhao, X., and Zhai, Z. (2022). Resilience in Infrastructure Systems: A Comprehensive Review. Buildings, 12.10.3390/buildings12060759 ↗
14. A Scientometric Review of Public-Private Partnership in Critical Infrastructure Resilience10.1088/1755-1315/1101/5/052007 ↗
15. Urban Critical Infrastructure’s Governance Framework for Climate Resilient Cities10.14207/ejsd.2020.v9n3p145 ↗
16. Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates10.1109/JSTARS.2022.3177025 ↗
17. Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.10.3390/rs11020196 ↗
18. Learning Spectral-Spatial Representations from VHR Images for Fine-Scale Crop Type Mapping: A Case Study of Rice-Crayfish Field Extraction in South China10.1016/j.isprsjprs.2023.03.019 ↗
19. Ghorbanzadeh, O., Crivellari, A., Ghamisi, P., Shahabi, H., and Blaschke, T. (2021). A Comprehensive Transferability Evaluation of U-Net and ResU-Net for Landslide Detection from Sentinel-2 Data (Case Study Areas from Taiwan, China, and Japan). Sci. Rep., 11.10.1038/s41598-021-94190-9 ↗
20. Benson, V., Robin, C., Requena-Mesa, C., Alonso, L., Carvalhais, N., Cortés, J., Gao, Z., Linscheid, N., Weynants, M., and Reichstein, M. (2023). Multi-Modal Learning for Geospatial Vegetation Forecasting. arXiv.10.1109/CVPR52733.2024.02625 ↗
21. Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.10.1007/978-3-319-24574-4_28 ↗
22. (2025, May 29). COPERNICUS Fire Severity 2025. Available online: https://forest-fire.emergency.copernicus.eu/about-effis/technical-background/fire-severity.
23. Xiao, T., Liu, Y., Zhou, B., Jiang, Y., and Sun, J. (2018). Unified Perceptual Parsing for Scene Understanding. arXiv.10.1007/978-3-030-01228-1_26 ↗
24. Zanaga, D., van de Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., and Fritz, S. (2022). ESA WorldCover 10 m 2021 V200 2022, Zenodo.
25. Bonafilia, D., Tellman, B., Anderson, T., and Issenberg, E. (2020, January 14–19). Sen1Floods11: A Georeferenced Dataset to Train and Test Deep Learning Flood Algorithms for Sentinel-1. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.10.1109/CVPRW50498.2020.00113 ↗
26. ResUNet-a: A Deep Learning Framework for Semantic Segmentation of Remotely Sensed Data10.1016/j.isprsjprs.2020.01.013 ↗
27. Ghorbani, Z., Khosravi, A., Maghsoudi, Y., Mojtahedi, F.F., Javadnia, E., and Nazari, A. (2022). Use of InSAR Data for Measuring Land Subsidence Induced by Groundwater Withdrawal and Climate Change in Ardabil Plain, Iran. Sci. Rep., 12.10.1038/s41598-022-17438-y ↗