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Geospatial Cloud Computing

Definition

Geospatial cloud computing runs storage and processing of spatial data in elastic cloud environments. It colocates data lakes, serverless functions, scalable databases, and GPU/CPU compute close to cloud‑optimized formats, reducing egress and speeding analysis. Infrastructure‑as‑code templates, notebooks, and workflow orchestrators turn heavy geoprocessing into reproducible, on‑demand jobs.

Application

Use cases include global satellite mosaics, nationwide parcel joins, large‑scale routing, and real‑time event streams. Teams scale up for peak processing, then scale down to control cost, while security controls protect sensitive layers.

FAQ

What formats are cloud‑native for rasters and vectors?

Cloud‑optimized GeoTIFFs and SpatioTemporal Asset Catalogs for imagery; Parquet/GeoParquet and vector tiles for large feature sets enabling columnar reads and tiling.

How do we manage cost in long pipelines?

Profile IO, cache intermediate results, use spot instances where safe, and prune unneeded outputs. Budget alerts and unit‑cost dashboards prevent surprises.

Is serverless viable for geoprocessing?

Yes for bursty, parallelizable tasks with modest memory and runtime; long heavy jobs may fit better on batch clusters. Hybrid designs are common.

How do we secure sensitive geodata in the cloud?

Encrypt at rest and in transit, segment networks, enforce least‑privilege roles, and use data‑access logs. Tokenized or differential‑privacy views protect analysis outputs.