Parcel geometry, building footprints, zoning attributes, and proximity scores: clean spatial features ready for AVM engineering, geospatial ML, and spatial ETL.
You need parcel boundaries joined with flood zones, building footprints, and census demographics for a 500,000-row training set. Today that means downloading four datasets, converting coordinate systems, writing spatial joins, and debugging geometry errors. The modeling takes a day. The data prep takes a month.
Pull spatial attributes (lot size, zoning, proximity scores, flood zone) for every address in a training set.
Generate feature vectors from spatial data: distance to POIs, land use classification, neighborhood density.
Replace custom spatial join pipelines with API queries. No PostGIS, no projection headaches, no batch jobs.
Test spatial hypotheses before committing to a full pipeline. Query a sample, validate the signal, then scale.
Pay per query, not per download. Fractional access to any geography.
Skip the spatial join pipeline. BigGeo AI lets Claude or ChatGPT pull clean spatial features for any address list, ready for your models. Governed, auditable, billed per query.
Learn About BigGeo AIBook a 20-minute demo. We'll walk through your use case with real data.