Obtaining highly reliable information about flooding events on a global scale currently requires the manual review and integration of multiple sources. There is a great variety of data, each part of which may or may not be relevant for a particular scenario, and each with different access mechanisms. Given that floods are both the most deadly and most costly natural hazards, this project will integrate flood inundation information from multiple sources into the DisasterAWARE (All-hazard Warnings, Analysis, and Risk Evaluation) platform, providing a single source of global information on floods that is supported by a common, normalized data model. End users will no longer be required to extract and merge data from multiple sources by hand as this will be done automatically by the middleware.

By using a model-of-models approach, which will include innovative new interferometric synthetic aperture radar. Using SAR-based sources as well as existing third-party sources, we will furthermore create a repository of flood information that will potentially be greater than the sum of its parts, providing higher levels of confidence and supplemental information than any single source. Our model-of-models approach will apply recent innovations in machine learning methods to create a unified picture that will become progressively better over time as more data become available.

Furthermore, by integrating with DisasterAWARE, the project will create a situational awareness tool that will specifically identify flood events and push this information to end-users through various mechanisms. Conversely, end-users will be able to register for notifications about events detected by the system in areas of interest. This will streamline the delivery of data and remove the requirements for navigating through multiple sources. End users will be presented with the data of interest immediately.