{"id":34782,"date":"2026-04-15T12:04:56","date_gmt":"2026-04-15T12:04:56","guid":{"rendered":"https:\/\/www.ogc.org\/?p=34782"},"modified":"2026-04-21T04:37:26","modified_gmt":"2026-04-21T04:37:26","slug":"from-research-to-implementation-building-shared-infrastructure-for-an-automated-world","status":"publish","type":"post","link":"https:\/\/www.ogc.org\/blog-article\/from-research-to-implementation-building-shared-infrastructure-for-an-automated-world\/","title":{"rendered":"From Research to Implementation: Building Shared Infrastructure for an Automated World","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"\t\t
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For several years, OGC has been asking an important question: how must geospatial data and processing offerings \u2014 and the standards they rest on \u2014 change as the world moves from systems that rely on human intervention to systems that talk directly to one another?That work, carried out through the Rainbow research initiative with the support of the European Union\u2019s Horizon Europe programme and strategic partners including ESA, NRCan, UKHO, and NGA, is now complete.<\/p>

Its lessons are clear: standards written for human readers do not scale to a world where machines must interpret and act on them directly.OGC’s implementation phase is built around that insight \u2014 focused on shared infrastructure that is modular, traceable, and designed to be read by both people and machines.<\/p>

Strengthening the Foundation: Building Blocks and Profiles<\/h2>

OGC Rainbow produced two closely related ideas \u2014 Building Blocks<\/strong> and Profiles<\/strong> \u2014 both registered as modular, machine-readable components in the OGC Definition Server.<\/p>

A Building Block<\/strong> is a self-contained specification component \u2014 a data model fragment, an API pattern, a set of constraints, or a code list \u2014 packaged for independent reuse. Each one bundles everything a developer or a machine needs to work with it: schemas in one or more formats, validation rules, tested examples, human-readable documentation, semantic mappings, and machine-readable metadata declaring its dependencies on other Building Blocks.<\/p>

This addresses a persistent problem in standards work, where overlapping definitions create unnecessary complexity and divergence. With Building Blocks, common elements are defined once, maintained in one place, and reused with consistency.<\/p>

A Profile<\/strong> is a constrained \u2014 and sometimes extended \u2014 version of one or more base standards, assembled from Building Blocks for a specific community, jurisdiction, or application. Critically, a profile is not a fork. It maintains a formal, machine-readable relationship to the standard it profiles, and any data conforming to a profile must also conform to the underlying standard.<\/p>

Profiles can be layered: a national mapping agency may profile a regional standard, which itself profiles a global OGC standard, with each layer adding specificity without breaking conformance to the layers beneath it. Because every profile is a first-class registered asset with its own identifier and machine-readable description, a consumer \u2014 human or machine \u2014 can programmatically trace the full inheritance chain and understand exactly what a conformance claim guarantees.<\/p>

Think of Building Blocks as standardised parts off a shelf, and Profiles as the assembled configurations tailored to a particular job \u2014 each with its parts list intact and inspectable.<\/p>

Shared Resources, Not Competing Software<\/h2>

OGC is not building proprietary software or end-user applications. It is producing the starter kits of the geospatial world \u2014 low-level, shareable code and reference implementations, released under the Apache-2.0 licence through public Git repositories.<\/p>

Equally important is where these assets live. Building Blocks, Profiles, reference implementations, vocabularies, and validators are all published through interconnected registers \u2014 authoritative, curated catalogs that make them discoverable, addressable, and traceable.<\/p>

Registers provide more than a place to find things: they carry provenance and governance, so every asset has a defined identity, a managed lifecycle, and declared relationships to others. For a developer or an AI agent, that means it is possible not only to find a component, but to know where it came from, how it has been maintained, and what it has been tested against.<\/p>

The benefits fall neatly along three lines. For commercial vendors, these resources can be picked up and integrated into products without restriction, compressing the journey to market. For the broader community, contributions flow back into a commons that grows more robust with every addition. And for the public interest, what becomes discoverable and reusable is also authoritative \u2014 governed by the same rigorous processes as any OGC standard<\/a>.<\/p>

The Real-World Use Case: Housing Crisis<\/h2>

The true test of any infrastructure is what it enables. Consider the Digital Building Permit<\/strong>. Across much of the developed world, the shortage of housing \u2014 especially for younger people \u2014 is not simply a matter of land or capital. It is also a matter of bureaucratic friction: slow, fragmented approval processes that add months, sometimes years, to construction timelines.<\/p>

The culprit is often mundane. Data is printed from one departmental system only to be typed manually into another. Different agencies apply different definitions of the same concepts. The result is delay, error, and duplication \u2014 at scale.<\/p>

By assembling Building Blocks for semantics (common definitions of building height and use class), data models (standardised permit applications), and ontologies (how a structure relates to its environment), OGC\u2019s framework makes automation possible. Interoperable blocks allow existing agency systems to exchange data directly, bypassing the print-and-retype loop entirely.<\/p>

A common language for location, building type, and environmental constraints means no more miscoding or redundant checks across departments. And streamlined workflows shorten the path from application to groundbreaking \u2014 more homes built sooner means less scarcity driving up prices.<\/p>

Why This Matters for an AI-Driven Future<\/h2>

Interoperability failures are rarely caused by a shortage of data. They are caused by ambiguous semantics \u2014 assumptions that everyone leaves implicit and no machine can safely decode.<\/p>

The cost is real and well documented. NASA\u2019s Mars Climate Orbiter was lost because one system expressed thruster force in pound-force seconds while another expected newton-seconds. During Hurricane Katrina, emergency responders could not effectively share geospatial data because agencies used incompatible coordinate reference systems. The more common cases are quieter but just as consequential: a date like \u201c03\/04\/25\u201d read differently across regions, or an elevation value with no declared vertical reference system introducing errors of tens of meters.<\/p>

As automation deepens, the cost of that ambiguity compounds. Artificial intelligence does not dissolve the requirement for structured data; it intensifies it. A model cannot guess what a dataset represents \u2014 it needs explicit, accessible definitions, which is precisely what OGC Building Blocks are designed to provide.<\/p>

There is a second reason Building Blocks matter for AI, and it is about trust as much as semantics. A common temptation with AI is to throw an entire, unbounded problem at a large model and hope for a usable answer. The trouble is that large solutions are nearly impossible to validate.<\/p>

Building Blocks offer a different path: decompose complex problems into small, encapsulated pieces. Each piece can be handled by a focused agent, and \u2014 critically \u2014 each result can be tested and verified, one component at a time. Trust is built incrementally rather than assumed wholesale.<\/p>

This approach is durable: it works whether the task is handled by AI agents, experienced professionals, or retrieval-augmented pipelines querying standardised data models.<\/p>

This framework, in short, is OGC\u2019s contribution to an AI-ready geospatial infrastructure<\/strong>: one that is discoverable, reusable, authoritative, governed, and built to be verified piece by piece.<\/p>

With the research phase complete, the full Rainbow Research paper is available<\/strong> HERE<\/a>.<\/strong><\/i><\/p>

Moving Forward: Delivery in Helsinki<\/h2>

OGC Connect Helsinki, the next OGC Member Meeting (June 1\u20134), is where this work moves from framework into practice. The focus will be on how Building Blocks are registered and applied across four active use cases: Climate and Environment, Marine Environment, Digital Building Permits, and Land Management Systems.<\/p>

Full details and registration: events.ogc.org\/OGCConnectHelsinki<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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