Geospatial Entity Resolution

Coordinate-agnostic matching of 3D buildings across independent city datasets

When disaster strikes, emergency teams must align newly captured UAV 3D building meshes with existing 3D city models - often without reliable GNSS or shared coordinates. This dashboard demonstrates an entity resolution pipeline that matches buildings purely by geometric shape, providing first responders with accurate situational awareness in damaged urban areas.

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How It Works?

Two 3D buildings matched across datasets

Matching Buildings Across Datasets

Two city surveys may model the same building independently, with no shared identifiers. The system determines which record in one dataset refers to the same real-world structure in another, relying solely on the geometry of the 3D models.

Works Without GPS or Shared Coordinates

When datasets originate from different sources, they often use incompatible coordinate systems. Traditional matching methods fail in these cases because they compare coordinates directly. The pipeline matches buildings purely by their 3D shape, so coordinate misalignment is not an obstacle.

3D viewer showing buildings colour-coded by match result

Reads Full 3D Geometry

Buildings are stored as CityJSON files, a standard format for 3D city models. The pipeline processes the complete 3D surface geometry of each building rather than reducing it to a single point on a map, capturing shape details that a point representation would miss.

Geometric blocking: nearest-neighbour search selecting candidate pairs from the Index dataset

Efficient Candidate Shortlisting

Comparing every building in one dataset against every building in another is not feasible at scale. The blocking step pre-selects a small set of geometrically similar candidates for each building, so the classifier processes only the most probable matches.

The Pipeline

Coordinate-agnostic geospatial ER pipeline: training and inference over candidate and index datasets, with featurization, blocking, matching, and rigid registration — Coordinate-agnostic geospatial ER pipeline overview: training (top) and inference (bottom) over candidate and index datasets, with featurization, blocking, matching, and rigid registration.

Geometric Featurization

Each building is summarised as a set of geometric measurements: area, height, perimeter, compactness, vertex count, and others. These measurements form a compact numerical representation of the building's shape that a machine-learning model can process.

Geometric Blocking

For each Candidate building, the blocking step selects a short list of the most geometrically similar buildings from the Index dataset. Only these candidate pairs proceed to the classifier, reducing the total number of comparisons from millions to a few per building.

Match Classification

A machine-learning classifier scores each candidate pair and outputs a confidence value. Pairs above the decision threshold are labelled as matches. The demo displays the results as a colour-coded map of the city, showing confirmed matches, false positives, and buildings with no match found.

Registration

For post-disaster UAV files without CRS metadata, Step 4 estimates a rigid 3D transform using anchor buildings and aligns the damaged candidate set to the index geometry. The output is written with the index dataset CRS so it can be rendered and compared in the correct coordinate reference system.