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Building Footprint Observations — String Normalisation & Missing-Value Fill
x402
Building Footprint Observations — String Normalisation & Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-31).
Normalises strings and fills missing values in building footprint observation records (footprint_m2, height_m) for up to 200 rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill 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 covering: (1) string normalisation results for station_group and other string fields across submitted rows, and (2) explicit missing-value fill results for the specified numeric field (footprint_m2 or height_m) using the chosen method (zero, mean, or median). Results are based entirely on the supplied rows with no external lookups.
Example use cases
Cleaning survey data before analysis
“I have 50 building survey records with messy station group labels and some null footprint_m2 fields — can you normalise the strings and fill the missing footprint_m2 values using the median of the provided rows?”
Pre-processing height data for a model
“Before I feed my building dataset into a model, I need the height_m nulls filled using the mean and the string fields normalised — here are 120 rows with row_ids, dates, station groups, footprint and height values.”
Auditing urban building records
“Can you clean up these 30 urban building observation records? Normalise the station_group strings and fill any missing height_m values with zero so I have complete, consistent data to work with.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-037?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have building footprint observation data (footprint_m2, height_m) that needs both string normalisation and explicit missing-value imputation in a single offline pass without external API calls or live predictions. It is ideal for small-to-medium batch jobs (up to 200 rows) where you need deterministic, reproducible cleaning using simple statistical fill methods (zero, mean, median) and want two structured reports for auditability.
Side effects
- Rows array empty or exceeds 200-item limit — validation error returned
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Unknown fill method value outside zero/mean/median enum triggers schema error
- Unknown field value outside footprint_m2/height_m enum triggers schema error
- All values for chosen field are null — mean/median fill cannot be computed
- row_id missing or blank causes required-field validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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