Use Cases
Seven detailed examples demonstrating how machine-readable standards, profiles, building blocks, and registers solve real-world problems — and what becomes possible for AI agents.
Offshore Wind Farm Policy Evaluation
The Problem
A national government must evaluate cross-sectoral impacts of offshore wind energy policies: energy output versus targets, effects on commercial fishing yields, changes in coastal tourism revenue, displacement of bird migration corridors, and effects on seabed ecology. This requires defining composite indicators that aggregate measurements from completely different domains — energy production telemetry, AIS vessel tracking, species observation records, hotel booking statistics, and bathymetric surveys.
Each domain uses its own feature types, observation models, spatial reference conventions, and temporal granularity. A marine biologist's "survey area" and a fisheries authority's "fishing zone" may overlap spatially but are defined in incompatible schemas.
Building Block Decomposition
An Observation Building Block (reused from OGC OMS) provides the semantic core. Its
context.jsonld maps observedProperty to sosa:observedProperty and unitOfMeasure to
qudt:unit — so an energy production reading in MWh and a fish catch weight in tonnes both
carry machine-interpretable unit declarations through the same structural pattern.
A Wind Farm Feature Type Building Block defines the schema for an offshore installation. A Composite Indicator Building Block defines a schema for policy indicators that formally declare inputs (references to other building blocks by URI), computation methods, output units, and temporal aggregation rules.
Profile Layering
Domain-specific profiles — Marine Ecology Observation Profile, Fisheries Observation Profile, Tourism Statistics Profile, Energy Production Profile — each constrain the base observation building block with their own vocabulary bindings and SHACL shapes. A Wind Farm Policy Evaluation Profile sits at the top, declaring dependencies on all four domain profiles plus the composite indicator building block.
What Becomes Possible
A policy analyst or AI agent can discover all relevant data sources, verify compatible semantics through common building blocks, compute composite indicators using formally defined formulas, and trace every result back to its source measurements with full provenance — all programmatically.
Digital Building Permits
The Problem
A developer in Stuttgart submits a building permit as a BIM model (IFC format). The municipality must check it against zoning regulations, setback requirements, height limits, floor-area ratios, fire safety, environmental constraints, and utility easements. Currently this takes weeks or months. Worse, the software vendor must create bespoke integrations for each of Germany's ~11,000 municipalities because they use different terminology, data models, and regulatory structures. One municipality says "fences," another "parcel boundaries," a third "lot lines" — for the same concept.
Building Block Decomposition
A Parcel Boundary Building Block maps different municipal property names to the same RDF predicate through JSON-LD contexts — solving the terminology problem by construction. A Building Footprint Building Block defines the BIM-derived ground-level outline. Constraint building blocks — Setback Constraint, Height Limit, Floor-Area Ratio, Fire Safety Clearance — are rule building blocks whose SHACL shapes validate referential integrity.
Profile Layering
A German National Building Regulation Profile composes all constraint building blocks with national defaults (ETRS89/UTM, DHHN2016, BauNVO). A Baden-Württemberg State Profile adds Landesbauordnung fire safety rules and hillside building regulations. A Stuttgart Municipal Profile binds to local Bebauungsplan zones and flood designations. Software vendors build once against the national profile and adapt incrementally for each municipality.
What Becomes Possible
An AI-powered permit system retrieves the municipal profile, assembles the complete SHACL shape set, applies the IFC transform to extract the building footprint, retrieves the parcel geometry from the cadastral API, and produces a structured compliance report — referencing each constraint by its building block URI with pass/fail results.
Climate Data for Urban Planning & Forestry
The Problem
Climate models produce data at 12.5 km resolution in CF-NetCDF with domain-specific conventions (rotated pole grids, 360-day calendars, kg m⁻² s⁻¹ for precipitation). Urban planners need sub-kilometer climate indices linked to their municipal data; foresters need species growth projections at 50×50m resolution fused with soil, terrain, and satellite data. Neither community can use the raw science data.
The gap is semantic, not just format. CF variable names ("tas", "pr") are opaque to planning software. CRS, calendars, units, and classification vocabularies all differ across climate science, EO, municipal GIS, and forest inventory systems.
Building Block Decomposition
Five building blocks: Climate Variable (CF Standard Names → URIs, QUDT units, calendar type declaration), Climate Index (computed indicators with declared algorithms and ensemble statistics), Statistical Downscaling (AI model registration with uncertainty quantification), Forest Site Assessment (species taxonomy URIs, soil classification, historical growth), and Urban Climate Vulnerability (morphology + population + exposure + composite index).
What Becomes Possible
A scalable pipeline where climate outputs are published once, transforms registered once, and new municipalities are onboarded by creating a profile rather than rebuilding the pipeline. Full machine-interpretable semantic chain for AI agents from raw climate model output to actionable local assessment.
Biodiversity Reporting under EUDR
The Problem
The EU Deforestation Regulation requires linking a commodity's production location to a plot of land and demonstrating no association with deforestation. This sounds simple but requires connecting supply-chain actors, production units, geospatial boundaries, temporal baselines, forest interpretations, biodiversity indicators, evidence packages, and due diligence statements. Terms like "habitat extent" or "forest degradation" may sound concrete but are not defined precisely enough for consistent operational use.
Why Building Block Decomposition Helps
EUDR reporting is a combination of smaller recurring problems: identifying production plots, linking them to supply-chain assets, defining a forest baseline, expressing a biodiversity indicator, recording uncertainty, capturing provenance, and validating results. A building block approach makes assumptions visible, allows individual elements to evolve independently, and supports reuse across commodities, jurisdictions, and reporting contexts.
Profiles Needed
An EUDR Compliance Profile defines mandatory elements. Commodity-specific profiles (cocoa, coffee, soy, rubber, cattle, wood) address differing supply chains. Biodiversity interpretation profiles define which baseline, concepts, scale, and uncertainty reporting apply. Jurisdictional/corporate profiles capture national practices or company-specific commitments while preserving traceability.
What Becomes Possible
A company reports not only a conclusion but the exact baseline, interpretation profile, indicator definition, transformation chain, and provenance that produced it. Different providers can coexist without lock-in because their assumptions are explicit and comparable. AI systems support explanation, validation, and analysis because they no longer need to guess what the data means.
Defense Geointelligence: Coalition Data Integration
The Problem
A NATO-allied nation contributing to a multinational operation relies on commercial satellite imagery, terrain data from a partner nation, AI-extracted features from a third ally's systems, and its own ground-truth observations. All must be fused into a trusted common operational picture — in a secure, often air-gapped environment where internet-based discovery is unavailable.
Each contributor uses different feature type catalogs, classification vocabularies (both data and security classification), coordinate reference systems, and positional accuracy conventions. Commercial providers are not bound by NATO STANAGs; AI-extracted features arrive with provider-specific taxonomy labels.
Building Block Decomposition
A Feature Observation Building Block provides the semantic foundation with mandatory CRS declaration, temporal validity, source characterization, and positional accuracy. A Confidence and Trust Metadata Building Block handles source reliability (adapting the NATO Admiralty Code as machine-readable URIs), AI model confidence scores, provenance chains, and information security classification. A National Feature Data Dictionary Mapping Building Block uses SKOS mapping predicates. A Positional Accuracy Harmonization Building Block canonicalizes between CEP, RMSE, CE90, and other conventions.
Air-Gapped Operation
Before deployment, the coalition's register infrastructure is replicated to a secure, air-gapped environment with all building block definitions, vocabulary registers, and transforms — each carrying provenance metadata with authoritative source URI, version, retrieval timestamp, and cryptographic hash. AI agents operate against the local cache exactly as they would against the networked register.
What Becomes Possible
An AI fusion system validates and harmonizes three data packages — commercial imagery, allied vector data, and ground-truth observations — using their respective profiles and transforms. For each feature in the fused product, the full provenance chain is recorded: source, profile, transform, original and harmonized accuracy, confidence rating, and whether any mapping involved approximation. Analysts can query: "show me all features where the feature type mapping was approximate" or "show me all elevation values transformed from a different vertical datum."
Integrated Cadastre, Land Use, and Deed System
The Problem
A country wants to unify its cadastral records (who owns which parcel), land use regulations (what activities are permitted), and deed registry (the legal chain of ownership) into an integrated system. Citizens, notaries, banks, tax authorities, and planning agencies all need different views of the same reality. Today, a human must manually cross-reference three systems with different identifiers, coordinate systems, and no machine-readable links between them.
Building Block Decomposition
A Parcel Identifier Building Block serves as the semantic anchor — the same parcel referenced in the cadastre, land use plan, and deed registry resolves to a single unambiguous identity. A Parcel Geometry Building Block defines survey-grade polygons with mandatory CRS and accuracy metadata. An Ownership Record Building Block defines ownership with encumbrances (mortgage, easement, usufruct as nested building blocks) and ISO 8601 temporal validity. A Land Use Designation Building Block binds categories to registered vocabularies.
Cross-Domain Constraints
A National Integrated Land Administration Profile adds SHACL constraints enforcing referential integrity across the three systems: every deed must reference an existing parcel; every land use designation must reference a temporally current geometry; every ownership transfer must reference both a valid deed and a parcel.
What Becomes Possible
A notary processing a property sale queries a single integrated view. An AI agent assisting with mortgage underwriting automatically verifies the parcel exists, is correctly delineated, has no conflicting encumbrances, and conforms to declared land use.
Cross-Border Flood Risk Assessment
The Problem
The Rhine flows through Switzerland, France, Germany, and the Netherlands. An extreme rainfall event in the Swiss Alps requires a coordinated flood model that seamlessly combines terrain data, land cover, hydrological measurements, and critical infrastructure from all four countries — each using national coordinate reference systems, height systems, land cover classifications, and hydrological observation conventions.
Building Block Decomposition
A Terrain Elevation Building Block requires explicit declaration of both horizontal and vertical CRS — directly addressing the ellipsoidal vs. orthometric height ambiguity. A Hydrological Observation Building Block profiles OGC OMS for discharge rate, water level, and precipitation intensity with mandatory units and minimum 15-minute temporal resolution. A Land Cover Building Block carries classification system URI declarations enabling machine-readable vocabulary mapping.
National Profiles and Registered Transforms
Each country has its own terrain profile: Swiss (LV95 + LN02), German (ETRS89/UTM32 + DHHN2016), French (RGF93/Lambert-93 + NGF-IGN69), Dutch (Amersfoort/RD New + NAP). CRS transformations between national systems are registered with source profile, target profile, transformation method (EPSG code), and accuracy bounds — all tested against known control points in the CI pipeline.
A vocabulary mapping transform converts between the four national land cover classification
systems via SPARQL CONSTRUCT queries using a registered EAGLE mapping table. Imperfect mappings
produce skos:closeMatch rather than skos:exactMatch, preserving information about mapping
confidence.
What Becomes Possible
When heavy rainfall is forecast, an AI early warning system automatically discovers relevant national datasets, applies registered transformations to create a unified cross-border terrain and land cover model, retrieves and validates real-time hydrological observations from all four countries, applies temporal alignment to a common 15-minute grid, and feeds the unified dataset into the flood model. The entire provenance chain is traceable through building block URIs and register entries. Seamless flood risk maps across all four countries — produced in minutes rather than the weeks of manual harmonization currently required.