Integrating 3D city data through knowledge graphs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CityGML is a widely adopted standard 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 for analysis in various applications. Nevertheless, the potential of querying CityGML data has not been fully exploited. The official GML encoding of CityGML is mainly an information model used for data storage and exchange, but not suitable for performing complex queries. The most common way of dealing with CityGML data is to store them as tables in the 3DCityDB system. However, it remains a challenging task for end users to formulate SQL queries over 3DCityDB directly for their ad-hoc analytical tasks because of the gap between the semantics of CityGML and the relational schema adopted in 3DCityDB. 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 to perform queries combining information from multiple data sources. In this work, we describe a CityGML-KG framework to expose the CityGML data in 3DCityDB as a KG. To evaluate our approach, we use CityGML data from the city of Munich as a test area and integrate OpenStreetMap data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it