StaR Maps: Unveiling Uncertainty in Geospatial Relations

TL;DR

This paper introduces StaR Maps, a probabilistic representation for uncertain geospatial data that enhances understanding and reasoning in complex urban environments, demonstrated through real-world experiments.

Contribution

The paper presents StaR Maps, a novel probabilistic, relational map representation that captures uncertainty and supports high-level reasoning in geospatial data analysis.

Findings

Effective representation of uncertain geospatial knowledge

Scalable computation for large urban areas

Successful application on crowd-sourced data

Abstract

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.