Introduction

Enabling AI agents to perform complex spatial analysis tasks such as identifying new city parks within a five‑minute walk of a school requires a reliable, standards‑compliant map engine. The OGC API - Processes specification provides exactly that: a well‑defined set of HTTP endpoints that any client, including AI systems, can invoke consistently.

This post documents the backend I built for my Google Summer of Code (GSoC) 2026 application with 52°North. I focus on the technical decisions, the trade‑offs I evaluated, and how you can reproduce a production‑grade, OGC‑compliant spatial service.

The Code Challenge

Task 1: Deploy and rigorously configure a backend that fully implements the OGC API – Processes standard, including containerization and production considerations.

Task 2: Validate the setup by implementing a meaningful geospatial process, registering it, and exercising the complete execution lifecycle (submission, asynchronous monitoring, and result retrieval).

The following sections walk through my approach, the stack I selected, and the architectural reasoning behind each choice.

Approach Task 1

Before writing any code, it is essential to understand what the OGC API – Processes standard actually defines.

The specification establishes a common contract for exposing geoprocessing capabilities over HTTP. By adhering to it, we avoid the fragmentation that would otherwise force every GIS backend to invent its own API, making it very difficult for AI agents or generic clients to interoperate.