Publication
StaR Maps: Unveiling Uncertainty in Geospatial Relations
Simon Kohaut; Benedict Flade; Julian Eggert; Devendra Singh Dhami; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2412.18356, Pages 1-8, arXiv, 2024.
Abstract
The growing complexity of intelligent transporta-
tion systems and their applications in public spaces has in-
creased 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, prob-
abilistic 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.