November 15 @ 8:00 pm – 9:00 pm UTC
This webinar will present the following reports by the WorldFAIR case study on Disaster Risk Reduction:
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.
Both of our case study countries are using remote sensing data which were assessed as having the richest metadata and met most of the FAIR metrics used in the assessment. Strategies for exploiting this data are discussed as they have great potential to provide up to date information during an emergency and to fill gaps in DRR data.
An essential task for any scientific discipline is the establishment of common standards and terminologies. Historically, standards have differed considerably with agencies creating standards and vocabularies based on their own use cases and priorities; consequently, there is currently no universal standard used by all DRR practitioners. We discuss the most widely used standard definitions and provide suggestions for harmonising standards. As both the United National Nations Office for Disaster Risk Reduction (UNDRR) and the World Meteorological Organisation (WMO) have been working toward improving the FAIRness and consistency of DRR data, we describe their efforts and outline their lessons learned and recommendations. Our next deliverable, which discusses metadata standards, controlled vocabularies, and ontologies, will add to this discussion.
While the current report focuses entirely on the DRR research area, DRR research is interdisciplinary by nature, encompassing researchers from earth sciences, climate change and environmental sciences, social studies, cultural information, and others. A key recommendation from the UNDRR is that there should be interdisciplinary collaboration when setting standards and definitions; therefore, increasing FAIRness in DRR has the potential to increase FAIRness across many related disciplines.
The study found that the data used by Fiji and Sudan for DRR is missing many key FAIR data elements. Hazard data tended to score highest for FAIRness, particularly hazard data originating from satellites. In contrast, vulnerability and exposure data were the least FAIR with little metadata and limited machine readability. However, there are some excellent regional and global initiatives aimed at increasing the level of FAIRness in DRR data. The UNDRR is currently reinventing its DRR database to provide a much more coherent and consistent view of the state of DRR both globally and nationally. We applaud this project and believe that significant effort should be made by the global and regional agencies to work together to provide standards, controlled vocabularies, data distribution platforms, resources and guidance for all people working to reduce the impact of disasters.
Disasters are inherently complex with wide-ranging and cascading impacts. The exponential growth in data generated daily, coupled with the complex nature of disasters, means we are hitting the limits of humans’ capacity to fully exploit all the data available for disaster risk reduction (DRR). This can be addressed with well-designed, pretrained Artificial Intelligence (AI) algorithms that can analyse large, complex datasets and fuse heterogeneous data. However, machine-readable, semantically linked data is a precursor for the use of AI in DRR.
Nations possessing ample resources and technical proficiency are better positioned to leverage DRR data effectively, thereby potentially creating disparities in the accessibility and application of DRR data. Recent advances in technology – particularly remote sensing data, which is income-agnostic and provides global coverage – provide an opportunity to reduce DRR data gaps. Global DRR institutions should collaborate proactively with countries and regional institutions to ensure the provision of Findable, Accessible, Interoperable, and Reusable (FAIR) and open DRR data. This could help bridge any historical or emergent DRR data inequalities.
This deliverable 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 this report provide examples of the use of remote sensing data and AI for DRR. We hope that the ideas and suggested actions in this report can be used to transform raw DRR data to valuable insights and decisions that produce tangible reductions in the impact of disasters worldwide.
Speakers: Jill Bolland, Bapon Fakhruddin T+T (WP12)