VecMALT: Fixed-Capacity Global–Local Representations for Geospatial Vector Geometry

Encoding geospatial vector geometry for reuse requires a fixed-size code to retain both overall form and spatially localized detail. We introduce VecMALT, which stores a global embedding and a fixed number of local chart records, each pairing a feature with its spatial frame. Coverage-constrained measure allocation bounds token participation, jointly assigns evidence to charts, and supervises their support along sampled boundaries. Across five code widths, local capacity improves complex-polygon reconstruction, while six interventions reveal allocation dependent trade-offs. We also freeze the codes and train independent readers. Accurate reconstruction does not ensure easy attribute access: for VecMALT, chart-record reading reduces window-occupancy MAE by 31.8% relative to a flat-code MLP, and feature–frame reassignment raises error by 16.3% after reader retraining. On three public polygon cohorts, with geometries available during representation learning and task labels held out, record-aware readers reduce boundary-count MAE in all nine local-method/dataset comparisons. These results distinguish geometric fidelity, spatial support, and independent access as complementary tests of reusable geometry codes.