Next Generation Disaster Resilience: Integrating Cloud-Native GIS, Deep Learning, and Geospatial Digital Twins

Abstract

Natural disasters are the fastest-growing global threat: floods alone cost 23% of all disaster-related economic losses, impacting 250 million people annually. While traditional emergency response relied on flat, desktop GIS on static two-dimensional spatial grids limited by compute power and siloed data. In this survey paper we review a radical paradigm shift that merges three distinct fundamental domain areas – cloud-native GIS architectures, deep learning algorithms (GeoAI), and high fidelity geospatial digital twins (GDTs). Together, this combination allows us to move beyond passive disaster recovery towards a proactive, predictive framework for disaster management. We review the dynamically fusing of the building information modeling (BIM) and GIS spatial, with the Internet of Things (IoT) telemetry at a locality with a cloud environment within hybrid local-cloud computing environments. We look at a spectrum of optimized spatial data standards that allow for a break from traditional scalability limits, such as cloud-optimized georeferenced tag-based files (COGs), spatio-temporal asset catalogs (STAC), geo-parquet and Zarr, to allow for streamlined data ingestion, with Zero-Trust based access controls. Cognitively we overview of GeoAI state-of-the-art techniques, from the application of the U-Net for Synthetic Aperture Radar (SAR) and its processing, convolutional damage mapping, Graph Neural Networks (GNNs) to model highly irregular infrastructure, to Hierarchical Reinforcement Learning to facilitate autonomic communications recovery. Finally we critically compare state of the art in neural rendering such as 3D Gaussian Splatting (3DGS) against limitations of traditional photogrammetry, to address the operational challenges posed by a mixed reality within edge environments. In conclusion, we establish a novel, integrated platform to realize active and predictive, closed-loop disaster recovery systems, at community scale.