CityGML is a widely adopted standard by the Open Geospatial Consortium (OGC)
for representing and exchanging 3D city models. The representation of semantic
and topological properties in CityGML makes it possible to query such 3D city
data to perform analysis in various applications, e.g., security management and
emergency response, energy consumption and estimation, and occupancy
measurement. However, the potential of querying CityGML data has not been fully
exploited. The official GML/XML encoding of CityGML is only intended as an
exchange format but is not suitable for query answering. The most common way of
dealing with CityGML data is to store them in the 3DCityDB system as relational
tables and then query them with the standard SQL query language. Nevertheless,
for end users, it remains a challenging task to formulate queries over 3DCityDB
directly for their ad-hoc analytical tasks, because there is a gap between the
conceptual semantics of CityGML and the relational schema adopted in 3DCityDB.
In fact, the semantics of CityGML itself can be modeled as a suitable ontology.
The technology of Knowledge Graphs (KGs), where an ontology is at the core, is
a good solution to bridge such a gap. Moreover, embracing KGs makes it easier
to integrate with other spatial data sources, e.g., OpenStreetMap and existing
(Geo)KGs (e.g., Wikidata, DBPedia, and GeoNames), and to perform queries
combining information from multiple data sources. In this work, we describe a
CityGML KG framework to populate the concepts in the CityGML ontology using
declarative mappings to 3DCityDB, thus exposing the CityGML data therein as a
KG. To demonstrate the feasibility of our approach, we use CityGML data from
the city of Munich as test data and integrate OpenStreeMap data in the same
area.

使用基于知识图谱的 CityGML KG 框架，将 CityGML 本体中的概念通过声明式映射到 3DCityDB 中进行填充，从而将其中的 CityGML 数据作为一个知识图谱公开。示范了该方法的可行性，并在慕尼黑市的 CityGML 数据中集成了 OpenStreetMap 数据。

通过知识图谱整合 3D 城市数据

Integrating 3D City Data through Knowledge Graphs

High-resolution optical satellite sensors, combined with dense stereo
algorithms, have made it possible to reconstruct 3D city models from space.
However, these models are, in practice, rather noisy and tend to miss small
geometric features that are clearly visible in the images. We argue that one
reason for the limited quality may be a too early, heuristic reduction of the
triangulated 3D point cloud to an explicit height field or surface mesh. To
make full use of the point cloud and the underlying images, we introduce
ImpliCity, a neural representation of the 3D scene as an implicit, continuous
occupancy field, driven by learned embeddings of the point cloud and a stereo
pair of ortho-photos. We show that this representation enables the extraction
of high-quality DSMs: with image resolution 0.5$\,$m, ImpliCity reaches a
median height error of $\approx\,$0.7$\,$m and outperforms competing methods,
especially w.r.t. building reconstruction, featuring intricate roof details,
smooth surfaces, and straight, regular outlines.

通过引入 ImpliCity，实现将三维场景表示为隐式连续占据场的神经表示，可以提高数字表面模型（DSM）的质量，特别是在建筑物重建方面具有明显优势。