Geospatial Edge Computing & IoT Gateway Processing

Built for the edge, not the data center

Traditional GIS assumes elastic compute, fast links, and abundant memory. The edge offers none of that. The material here is organized around the realities of gateway hardware: deterministic execution, graceful degradation, store-and-forward synchronization, and compiled hot paths that keep latency predictable under thermal and network stress. Whether you are an IoT engineer, a field GIS technician, or a Python developer moving spatial code onto devices, each guide is a deployment-ready reference rather than an abstract tutorial — with runnable code, measured numbers, and the failure modes each pattern actually produces in the field.

Start here

Six pages that cover the decisions most deployments get wrong first — sizing, ordering, and the numbers each one should be derived from rather than guessed.

• Size the budget before the hardware Peak working set, interpreter baseline, concurrency and headroom — the four numbers that decide the part order.
• Pick the right spatial index Grid, packed R-tree, cell ids, or no index at all — chosen from the data's shape rather than the algorithm's reputation.
• Make the spool survive the outage Reserved partitions, crash-safe appends and a drain whose cursor only moves on an acknowledgement.
• Smooth for decisions, simplify for storage Why the order matters, and what each pipeline stage is allowed to change about a track.
• Ship a basemap that fits the flash Read-only archives, coverage arithmetic per zoom level, and a writable cache with a wear budget.
• Update without a truck roll Signed bundles, atomic application, and an A/B rollback that reverts itself when a build does not confirm.