Disaster Risk Reduction

Harnessing data and hazard definitions and classifications is vital for disaster risk reduction, climate change adaptation and resilience, sustainable development, global health security and urban resilience. It is recognised as a crucial step in support of the Sendai Framework and the other UN landmark agreements of 2015-16.

The UNDRR/ISC Sendai Hazard Definition and Classification Review Technical Report supports these agreements by providing a common set of hazard definitions for monitoring and reviewing implementation which calls for ‘a data revolution, rigorous accountability mechanisms and renewed global partnerships’. An important step will be to make this vocabulary FAIR following the guidelines in the Ten Simple Rules.

Advances in technology have enabled a dramatic increase in the availability of satellite imagery and the power of geospatial services for DRR, yet significant challenges remain for the effective operationalization of these data for practical purposes, including societal use, policy making, rapid response etc. The FAIR principles are critical to facilitating the use of advanced technologies to extract pertinent information for DRR and climate adaptation and resilience. Developing countries face particular challenges in access and usability of data.

The application of the FAIR principles for EO data, including domain-specific FAIR vocabularies for disaster, climate change and global health for the Pacific and Africa, will facilitate the easier, and lower cost, reuse of data and the extraction of key information.

Partner

Work Package Lead

Jill Bolland, Tonkin+Taylor

Disaster Risk Reduction Featured Outputs

Disaster Risk Reduction Case study report (Deliverable 12.1)

This report describes the types of data used for disaster risk reduction (DRR) and provides two country case studies, for Fiji and Sudan, with an in-depth look at the DRR datasets and associated metadata used by each country. These datasets were assessed against 15 FAIR (Findable, Accessible, Interoperable, Reusable) data metrics to identify which elements of FAIR were met. The report also provides a broader context giving details on the national, regional, and global agencies providing or hosting DRR data as well as initiatives aiming to increase the FAIRness of DRR data.
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Disaster Risk Reduction Domain-specific FAIR vocabularies (Deliverable 12.2)

This report explores the use of vocabularies in the DRR domain and how controlled vocabularies coupled with ontologies can enhance the semantic value of DRR data thereby improving interoperability. Enhancing semantic interoperability would result in improved collaboration and communication within the DRR domain and facilitate collaborations with other scientific domains. The final sections of the report provide examples of the use of remote sensing data and AI for DRR.