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Building Footprint Observations — String Normalisation + Keyed Dataset Comparison
x402
Building Footprint Observations — String Normalisation + Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Normalises strings and performs keyed dataset comparison on up to 200 building footprint observation records (footprint_m2, height_m), returning 2 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — String normalisation + Keyed dataset comparison: String normalisation + Keyed dataset comparison for supplied building footprint observations records (footprint_m2, height_m). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two analytical reports: (1) a string normalisation report covering station_group and other text fields across the submitted rows, and (2) a keyed dataset comparison report showing value-level differences between the 'rows' and 'other' input arrays matched by row_id. No live data, predictions, or external lookups are included.
Example use cases
Cross-source footprint data reconciliation
“I've got two exports of building footprint measurements from different surveyors for the same sites — rows with row_id, station_group, date, footprint_m2, and height_m — can you normalise the strings and compare them side by side to find where the values differ?”
Station group naming audit
“Our building observation records have messy station_group labels across two datasets — run string normalisation on both and compare them by row_id so we can see which groups have inconsistent naming or mismatched footprint and height values.”
Before-and-after renovation comparison
“I have pre-renovation and post-renovation building footprint records for about 150 buildings, each with a row_id, date, footprint_m2, and height_m — please compare the two datasets by key and normalise any string fields so I can see what changed.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-036?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have two batches of building footprint observation records (up to 200 rows each) and need both string normalisation and a keyed row-by-row comparison in a single low-cost call. It is ideal for data reconciliation, survey auditing, and detecting discrepancies between two structured building datasets without needing live lookups or external data enrichment.
Side effects
- Input arrays exceed 200 rows — request will be rejected
- Missing required fields (row_id, station_group, date, footprint_m2, height_m) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- Numeric values outside the allowed range cause schema validation failure
- Empty rows array returns an error (minItems: 1)
- Mismatched row_ids between 'rows' and 'other' may result in incomplete comparison output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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