Cloud-Native Architectures for Large-Scale Remote Sensing and Geospatial Data Platforms

Abstract

Modern remote sensing platforms use cloud-native architectures with standards-based separation of control and data planes to achieve cost and performance targets at the petabyte scale. Cloud-native stacks are designed around Cloud Optimized GeoTIFF (COG), a byte-range streamable format, SpatioTemporal Asset Catalogs for discoverable metadata management, and Open Geospatial Consortium (OGC) standards for interoperable service delivery. Performance is illustrated in distributed compute engines, including large-scale spatial joins and analyses over multi-temporal data. Service layer design considerations include tile-first APIs and tiled data pipelines, alongside aggressive tile caching at the content delivery network (CDN) and edge layers. Performance considerations model tile latency, time to first pixel, and egress efficiency in distributed systems. These standards enable elastic scalability of platforms and interactive visualization workflows to meet the variability in analytics consumption patterns. Demonstrations have established that performance can be improved with internal tiling, multi-resolution overviews, and HTTP range requests that reduce bandwidth and latency in a wide variety of client ecosystems.