用多源数据生成城市3D位置先验，解决低空飞行缺建筑高度数据的问题。
Location Prior Generation via Multi-Source Urban Data Fusion for Low-Altitude Air Mobility
- 融合遥感、无人机、车辆轨迹和地图数据，构建可复用的城市3D位置先验
- 三层优先级赋高：标签、楼层数乘3.2米、类型默认值，误差约5.5米
- 质量门控机制自动识别成像异常，确保输出稳定可靠
全球地理空间数据库中超过95%的建筑物缺失高度信息。在新兴的低空经济中，这一数据缺口迫使空中平台依赖实时机载感知，而非预计算的三维场景。本文提出位置先验生成框架（LPGF），通过融合哨兵-2影像、无人机遥测、车辆GPS轨迹和开放街图轮廓，构建结构化、可复用的城市位置先验。该框架采用三级优先级赋高策略：（1）优先使用OSM明确高度标签；（2）有楼层数时按每层3.2米计算；（3）否则采用建筑类型默认高度，最差误差约5.5米。当满足四条件质量门控时，激活基于阴影的高度估计模块（SHEM）；任一条件不满足则转入结构化备用路径。在MiTra A50米兰数据集上，质量门控准确识别出两种成像失效模式：10米地面采样间隔下的亚像素阴影与0.93米时的地面阴影合并，均生成一致的27栋建筑先验。第三层级类型默认值经15栋人工楼层统计验证，平均绝对误差为3.07米，在5.0米不确定度范围内。结果表明，对普遍可用数据流进行结构化、质量门控融合，可有效推动低空城市运行的三维场景覆盖。
原文摘要 · Abstract (English)
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.
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