Semantic–geography integrated spatiotemporal safety mapping for urban low-altitude airspace environments

Abstract

Unmanned aerial vehicle (UAV) safety is becoming increasingly critical as UAV operations are integrated into urban service and transportation systems, creating an urgent need for risk representations that can support refined low-altitude airspace management. Existing urban safety maps are still mainly expressed as two-dimensional or height stratified representations, which cannot capture the continuous variation of risk with altitude and therefore remain inadequate for continuous low-altitude flight scenarios. This paper develops a continuous three-dimensional spatiotemporal safety mapping framework for urban low-altitude environments. The framework integrates semantic information, digital surface model (DSM) and digital terrain model (DTM) derived morphology, pedestrian related exposure, and meteorological conditions within a unified geo-referenced support. A vertical continuous interpolation function is introduced to transform discrete risk representations into a continuous three-dimensional spatiotemporal risk field, thereby enabling continuous risk analysis across space, altitude, and time. Experiments in a real urban scene show that risk values vary significantly across different regions, time periods, and scene conditions, and that the resulting field reveals clear vertical differentiation and temporal redistribution patterns that conventional planar safety maps cannot represent consistently. These findings show that the proposed reusable continuous three-dimensional and time varying risk model offers a practical basis for refined low-altitude airspace management and has the potential to support future high density UAV operations.