Establishing a Geospatial Discovery Network with efficient discovery and modeling services in multi-cloud environments

Abstract

The ballooning volume and complexity of geospatial data is one of the main inhibitors for advancements in climate & sustainability research. Oftentimes, researchers need to create bespoke and time-consuming workflows to harmonize datasets, build/deploy AI and simulation models, and perform statistical analysis. It is increasingly evident that these workflows and the underlying infrastructure are failing to scale and exploit the massive amounts of data (Peta and Exa-scale) which reside across multiple data centers and continents. While there have been attempts to consolidate relevant geospatial data and tooling into single cloud infrastructures, we argue that the future of climate & sustainability research relies on networked/federated systems. Here we present recent progress towards multi-cloud technologies that can scale federated geospatial discovery and modeling services across a network of nodes. We demonstrate how the system architecture and associated tooling can simplify the discovery and modeling process in multi-cloud environments via examples of federated analytics for AI-based flood detection and efficient data dissemination inspired by AI foundation models.