her_cross_reference
Cross-reference candidate locations against known heritage assets to classify each as match, near, or novel based on proximity.
Instructions
Cross-reference candidate locations against known heritage assets.
Takes a list of candidate locations (e.g. from LiDAR survey) and classifies each as match, near, or novel based on proximity to known NHLE records, AIM aerial mapping features, and optionally Heritage Gateway records.
Args: candidates: JSON array of {"easting": x, "northing": y} dicts match_radius_m: Distance threshold for "match" (default 50m) near_radius_m: Distance threshold for "near" (default 200m) designation_types: Comma-separated NHLE designation types to match against (e.g. "scheduled_monument,listed_building") include_aim: Include AIM aerial mapping features in known assets (adds monument_type, period, form from aerial archaeology) gateway_sites: JSON array of Gateway records with easting/northing (output of her_enrich_gateway) to merge into known sites output_mode: Response format — "json" (default) or "text"
Returns: Classification of each candidate as match, near, or novel
Tips for LLMs: - Input candidates as BNG easting/northing coordinates - "match" means the candidate is within match_radius_m of a known asset - "near" means within near_radius_m but not a match - "novel" means no known asset within near_radius_m - Set include_aim=true for LiDAR workflows to include aerial features - Use her_enrich_gateway first to resolve Gateway record coordinates, then pass the output as gateway_sites for richer cross-referencing - Use her_export_for_lidar to get known sites in the same area
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| candidates | No | [] | |
| include_aim | No | ||
| output_mode | No | json | |
| gateway_sites | No | [] | |
| near_radius_m | No | ||
| match_radius_m | No | ||
| designation_types | No |