Serverless Geospatial Processing, done right.
Authoritative engineering references for designing, deploying, and operating serverless raster and vector workflows. Built for cloud GIS engineers, Python backend devs, and platform architects who care about constraint-aware architecture, cost, and observability.
What this site is
Serverless platforms reshape how spatial data is ingested, transformed, and served. Functions replace fleets, queues replace cron, and chunked I/O replaces monolithic loads — but only if the architecture respects platform limits, package size budgets, and IAM boundaries.
Each reference here distils production patterns: event-driven shapefile ingestion, raster cold-start mitigation, dead-letter routing for vector jobs, packaging GDAL into Lambda layers, and least-privilege IAM across AWS, GCP, and Azure. Side-by-side platform comparisons — cold starts, timeout ceilings, ephemeral storage — help you pick the right provider per constraint.
The focus throughout is constraint-aware: memory, ephemeral disk, timeout, concurrency, and cost. End-to-end pipeline recipes tie the concepts together into runnable builds — tiling a 10 GB GeoTIFF with Step Functions, real-time AIS tracking, Sentinel-2 NDVI, and Cloud Run vector tiles. Examples lean on Python, GDAL, and the major-cloud event primitives, and emphasise observability and deployment automation over toy demos.
Explore the references
Event-Driven Patterns
Object-storage triggers, queue routing, batch-vs-stream, chunked I/O, and durable orchestration for spatial pipelines.
Read the guideArchitecture & Platform Limits
Memory and CPU sizing, ephemeral storage, GDAL cold-starts, and least-privilege IAM across AWS, GCP, and Azure.
Read the guidePackaging & Dependencies
Python layers, native compilation, Docker optimization, and CI/CD synchronization for geospatial dependencies.
Read the guidePipeline Recipes
End-to-end, runnable serverless recipes: 10 GB GeoTIFF tiling with Step Functions, real-time AIS tracking, Sentinel-2 NDVI, and Cloud Run vector tiles.
Read the guideStart here — featured pages
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Process a 10 GB GeoTIFF with Step Functions and Lambda
Pipeline Recipes
A complete tiling pipeline that fans out per-tile Lambdas under the 15-minute ceiling, then merges the outputs into a Cloud Optimized GeoTIFF.
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Cold Start Comparison: AWS vs GCP vs Azure
Architecture & Limits
Side-by-side cold-start behaviour and warm-keeping options for a Python GDAL stack across all three serverless platforms.
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S3 and GCS Event Triggers for Shapefiles
Event-Driven Patterns
How to wire object-storage events on AWS and GCP to trigger shapefile ingestion pipelines without polling.
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Batch vs Stream Geospatial Processing
Event-Driven Patterns
Decision framework for choosing batch or streaming execution for satellite ingest, AIS tracking, and sensor feeds.
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Cold-Start Mapping for Python GDAL
Architecture & Limits
Exact initialisation timelines and mitigation patterns — provisioned concurrency, slim layers, lazy imports.
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Ephemeral Storage Limits in AWS Lambda
Architecture & Limits
Managing
/tmpscratch space during GeoTIFF extraction, VRT assembly, and tile matrix generation. -
Python Layer Management and Size Reduction
Packaging & Dependencies
Deduplicating shared wheels, enforcing version pins, and stripping binaries to hit the 250 MB Lambda limit.
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Docker Container Optimisation for GIS
Packaging & Dependencies
Multi-stage Docker builds that keep GDAL/PROJ images lean for Cloud Run, Lambda containers, and Azure Functions.