Open Critical Infrastructure Exposure for Disaster Forecasting, Mitigation and Response
Abstract
Cities are complex systems with interconnected "lifeline" networks propelled by critical infrastructure, which can sever. Following Hurricanes Maria and Katrina and the Tōhoku earthquake and tsunami, damage to these systems resulted in cascading effects that severely impeded recovery and crippled regional economies. GIS data gives the location of critical infrastructure (CI) and can be used to identify and mitigate damage, but in many cases the location of key components are unmapped or unshared, particularly in developing countries. Without finding these assets, it is not possible to identify where the regional risk from infrastructure disruption stands to result in cascading effects which, in some cases, could significantly reverse progress in developing countries. "Open Critical Infrastructure Exposure for Disaster Forecasting, Mitigation and Response", funded under with a NASA Roses grant, will build on prior research characterizing the built environment to expand the ability to model the catastrophic impacts of infrastructure disruption by providing a foundation for CI exposure development with EO. The project will start in India and expand to developing countries globally, prioritizing based on end-user requirements. As with buildings, development is a data fusion process requiring collection of existing datasets and use of segmentation and edge detection algorithms to build skeletal networks, which are augmented statistically through stratified sampling. VIIRS and other sensors are used to identify major power plants along transmission corridors. Missing smaller facilities are distributed through a simulation process. The resulting data are suitable for the types of risk studies prioritized by the Sendai Framework for Disaster Risk Reduction that are currently being implemented by NGOs in developing countries. Data will be delivered openly and globally to developing countries and all those interested in risk, as well as integrated into commercial products for global risk identification and management.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN51C..10H
- Keywords:
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- 1640 Remote sensing;
- GLOBAL CHANGE;
- 1942 Machine learning;
- INFORMATICS;
- 4329 Sustainable development;
- NATURAL HAZARDS;
- 6620 Science policy;
- POLICY SCIENCES & PUBLIC ISSUES