用概率方法表达地图不确定性，让智能交通系统更可靠。
StaR Maps: Unveiling Uncertainty in Geospatial Relations
- 将地理关系与统计概率结合，构建不确定地图表示
- 在真实城市数据上验证了该方法对复杂信息的建模能力
- 适合需要高精度推理的交通、城市规划等场景
智能交通系统及公共空间应用日益复杂，对表达性强、灵活的知识表示需求激增。尽管现有制图工作已实现广泛覆盖并标注语义特征，但其固有的不确定性常被地理信息系统忽视。因此，亟需一种融合统计概率与地理关系性的表示方法，以真实反映数据精度，并支持高层次推理，获取任务相关的深层洞察。本文提出统计关系地图（StaR Maps），用于表征不确定的语义地图数据；同时展示其在大规模城市空间中的高效计算能力。基于真实众包数据的实验验证了该方法在表达不确定知识和复杂地理信息推理方面的有效性。
原文摘要 · Abstract (English)
The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is essential to understand their inherent uncertainties, which are commonly underrepresented by the respective geographic information systems. Hence, it is critical to develop a representation that combines a statistical, probabilistic perspective with the relational nature of geospatial data. Further, such a representation should facilitate an honest view of the data's accuracy and provide an environment for high-level reasoning to obtain novel insights from task-dependent queries. Our work addresses this gap in two ways. First, we present Statistical Relational Maps (StaR Maps) as a representation of uncertain, semantic map data. Second, we demonstrate efficient computation of StaR Maps to scale the approach to wide urban spaces. Through experiments on real-world, crowd-sourced data, we underpin the application and utility of StaR Maps in terms of representing uncertain knowledge and reasoning for complex geospatial information.
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