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Building Footprint Observations — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Building Footprint Observations — Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Runs three local analytical reports (record quality audit, string normalisation, and trailing mean series) on up to 200 rows of building footprint observation data (footprint_m2, height_m).
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied building footprint observations records (footprint_m2, height_m). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports: (1) a record quality audit flagging missing, out-of-range, or malformed values across rows; (2) a string normalisation report standardising station_group and other string fields; and (3) a trailing mean series for the selected numeric field (footprint_m2 or height_m) computed over the specified window. All computed locally with no external lookups.
Example use cases
Urban dataset pre-processing quality check
“I have 50 building footprint records from our urban survey — can you audit their quality, normalise the station group strings, and compute a trailing mean (window 4) on footprint_m2 so I can see trends before loading into our database? Here are the rows: row-1, site-north, 2026-09-01, 300, 12; row-2, Site North, 2026-09-02, 310, 13 ... (and so on up to row-50).”
Height data rolling average for construction portfolio
“Please run a trailing mean analysis with a window of 5 on the height_m field for these 30 building observation records, and also give me the quality audit and string normalisation reports so I can spot any dirty data before my report.”
Footprint anomaly detection prep
“I need to clean up and summarise 80 building footprint observation rows — run the record quality audit to find bad values, normalise the station_group strings, and compute a trailing mean series on footprint_m2 with window size 6. I'll paste the rows now.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined analytical operations — quality audit, string normalisation, and trailing mean series — on building footprint data in a single low-cost call. It is ideal for pre-processing pipelines that need to validate and clean small-to-medium datasets (up to 200 rows) without any external data lookups or live predictions. Prefer it over general-purpose analytics tools when the dataset is specifically building footprint observations with footprint_m2 and height_m fields.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Missing required fields (row_id, station_group, date, footprint_m2, height_m) causes per-row validation failure
- Window size outside 2–200 range causes parameter rejection
- Non-numeric values for footprint_m2 or height_m (unless null) cause processing errors
- Payment failure via x402 protocol blocks request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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