Location Prior Generation via Multi-Source Urban Data Fusion for Low-Altitude Air Mobility

2026-05-25

AI Summary

This study addresses the critical lack of building height data—missing for over 95% of global structures—which hinders low-altitude aerial systems reliant on preconstructed 3D environments. The authors propose the Location Prior Generation Framework (LPGF), introducing a structured, quality-gated multi-source fusion strategy that integrates Sentinel-2 imagery, UAV telemetry, vehicle GPS trajectories, and OpenStreetMap (OSM) footprints. Building heights are assigned via a three-tier rule hierarchy: OSM tags, floor-count inference, and type-based default values. A Shadow Height Estimation Module (SHEM) is selectively activated only when four stringent quality conditions are met. Without requiring dedicated 3D surveys, LPGF generates reusable urban 3D priors, consistently producing priors for 27 buildings in the MiTra A50 Milan dataset. Manual validation on 15 buildings confirms a mean absolute error of just 3.07 meters for type-default heights—well within the <5-meter uncertainty threshold.

Abstract

Building height, the third dimension (3D) of urban spatial data, is absent in over 95% of structures in global geospatial databases. For the emerging low-altitude economy, this data gap forces each aerial platform to rely on real-time onboard sensing rather than pre-computed 3D scene geometry. We present the Location Prior Generation Framework (LPGF), a multi-source data fusion pipeline that integrates Sentinel-2 imagery, UAV telemetry, vehicle GPS trajectories, and OpenStreetMap footprints into structured, reusable urban location priors. LPGF assigns building heights through a three-tier priority hierarchy: (1) explicit OSM height tags where available, (2) floor count multiplied by 3.2 m per story where recorded, and (3) building-type default heights otherwise, yielding a worst-case error of approximately 5.5 m. An optional shadow-based height estimation module (SHEM) is activated only when a four-criterion quality gate is satisfied; when any criterion fails, the pipeline routes to structured fallback. On the MiTra A50 Milan dataset, the quality gate correctly identified two imaging failure modes: sub-pixel shadows at 10 m GSD and ground shadow merging at 0.93 m GSD, producing a consistent 27-building prior in both cases. Tier 3 type-default heights were validated against manual floor counts (n=15), achieving MAE=3.07 m within the 5.0 m uncertainty bound. The framework demonstrates that structured, quality-gated fusion of universally available data streams can bootstrap 3D scene coverage for low-altitude urban operations.

Problem

• building height
• 3D urban data
• low-altitude air mobility
• geospatial database
• data gap

Innovation

• multi-source data fusion
• building height estimation
• quality-gated framework
• low-altitude air mobility
• urban 3D prior