3DCityKG: Knowledge Graph representation for Semantic 3D City Models
This repository provides a fully working implementation of the mapping of semantic 3D city models that are compliant to the CityGML 1.0/2.0/3.0 standard onto rich graph representations in the graph database system Neo4j. It has been used already in different projects like change detection and interpretation between two versions of the same city model (project citymodel-compare) as well as in multi-modal route planning using CityGML 3.0 (navigating from a specific room inside one building to a specific room in another building and traveling by car in-between including finding a parking space and the pedestrian route planning from the parking space to the target room in the destination building).
The CityGML import/export function of 3DCityKG supports reading and writing CityGML-compliant datasets given in the XML-based CityGML encodings of CityGML version 1.0, 2.0, and 3.0 as well as the CityJSON encoding. Imported data can be analysed, modified, and enriched using Neo4j's Cypher query language. The mapped 3D city models can interactively be explored using the Neo4j Browser, which is part of the Neo4j software ecosystem. 3DCityKG is also supplied as a Docker container for quick and easy installation.
This repository was split from the bigger project citymodel-compare. It is now a stand-alone library that can be used for simply creating a graph representation of a single city model as a basis for further graph-based analysis.
✔ Map 3D city models to knowledge graphs using Neo4j
✔ Support import for CityGML 1.0, 2.0, 3.0, and CityJSON
- For all modules and LoDs
- Including XLinks and implicit geometries
- Fast, multi-threaded import for CityGML
✔ Support export for CityGML 1.0, 2.0, 3.0, and CityJSON
- For all modules and LoDs
- CityGML datasets can be exported using a bounding box
This tool can be used in two ways: either via Gradle or via Docker. For testing purposes, Docker is recommended.
What is needed:
-
Make sure Docker is up and running.
-
Pull the following image from Docker Hub:
docker pull tumgis/3dcitykg:1.0.0
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Run the image:
# Linux docker run \ -it --rm \ -p 7474:7474 -p 7687:7687 \ tumgis/3dcitykg:1.0.0# Windows docker run ^ -it --rm ^ -p 7474:7474 -p 7687:7687 ^ tumgis/3dcitykg:1.0.0This will start a Neo4j instance with all necessary dependencies installed. The parameters are as follows:
-it: Interactive mode.--rm: Remove the container after it exits.-p 7474:7474: Expose port 7474 of the container to port 7474 of the host machine. This is the port used by the Neo4j browser (such as visualization and inspecting Cypher queries).-p 7687:7687: Expose port 7687 of the container to port 7687 of the host machine. This is the port used by the Neo4j Bolt connector (such as for RESTful services).
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The database is available at
neo4j://localhost:7687(Bolt protocol) and can be visualized using Neo4j Desktop or Neo4j Browser.
Notes: To simplify the process for testing purposes, the Docker container has already been loaded with a test dataset, which is the FZKHaus datasets.
Notes: Once started, the datasets will be automatically mapped and stored in the graph database. All from scratch, no existing Neo4j database instance is contained in the Docker container beforehand. Please refer to this section for more details on how to use your own datasets.
The entire program can be configured using a single file located at lib/config/run.conf. Example configurations for CityGML 1.0, 2.0, 3.0, and CityJSON datasets can be found in the examples sub-directory.
The most important settings are:
# USE CASE
# 0: Map only using the configurations in this file
# 1: Export only using an existing database configured in this file
# 2: Map AND Export using the configurations in this file
case = 2
# DATABASE CONFIGURATIONS
# .......................
# Keep database online
db.online = true
# MAPPER CONFIGURATIONS
# .....................
# Input dataset to map onto graphs, can have multiple files/directories
# If a path is a directory, ALL files in that folder shall be imported as one
mapper.dataset.paths = [
"input/citygml/fzk_haus_lod2_v2.gml"
]
# CITYGML CONFIGURATIONS
# ......................
# CityGML version
citygml.version = "v2_0"
# OR CityJSON
cityjson = false
# CityGML export
citygml.export.partition = 0
citygml.export.bbox = "457842.0, 5439083.0, 111.8, 457854.0, 5439093.0, 118.3"
citygml.export.path = "output/citygml/export_v2.gml"-
Make sure Docker is up and running.
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Pull the following image from Docker Hub:
docker pull tumgis/3dcitykg:1.0.0
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Clone the project:
git clone https://github.com/tum-gis/3dcitykg
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Place your CityGML or CityJSON datasets in the
input/citygmldirectory:lib ├── input │ └── citygml │ ├── dataset1.gml │ ├── dataset2.gml │ └── ... │ └── cityjson │ ├── dataset1.json │ └── ...
# Example cp /path/to/dataset.gml lib/input/citygml/ -
Configure the program using the
lib/config/run.conffile:mapper.dataset.paths = [ "input/citygml/dataset1.gml" ] -
Bind the
input,output, andconfigdirectory from the host machine to the Docker container:# Linux docker run \ -it --rm \ -p 7474:7474 -p 7687:7687 \ -v "/absolute/path/to/config:/home/gradle/src/3dcitykg/config" \ -v "/absolute/path/to/input:/home/gradle/src/3dcitykg/input" \ -v "/absolute/path/to/output:/home/gradle/src/3dcitykg/output" \ tumgis/3dcitykg:1.0.0
# Windows docker run ^ -it --rm ^ -p 7474:7474 -p 7687:7687 ^ -v "/absolute/path/to/config:/home/gradle/src/3dcitykg/config" ^ -v "/absolute/path/to/input:/home/gradle/src/3dcitykg/input" ^ -v "/absolute/path/to/output:/home/gradle/src/3dcitykg/output" ^ tumgis/3dcitykg:1.0.0
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The graph database can be found in the
/absolute/path/to/outputdirectory on the local host machine. -
The exported datasets will also be available in the
/absolute/path/to/outputdirectory on the local host machine.
To build the Docker image from scratch, run the following command:
docker build --no-cache -t tumgis/3dcitykg:1.0.0 .Tag and push the image to Docker Hub:
docker tag 3dcitykg tumgis/3dcitykg:1.0.0
docker push tumgis/3dcitykg:1.0.0This tool is part of the following publications:
Nguyen, Son H.: Automatic Detection and Interpretation of Changes in Massive Semantic 3D City Models. Dissertation, Technical University of Munich, 2024.
Nguyen, Son H.; Kolbe, Thomas H.: Identification and Interpretation of Change Patterns in Semantic 3D City Models. Lecture Notes in Geoinformation and Cartography - Recent Advances in 3D Geoinformation Science - Proceedings of the 18th 3D GeoInfo Conference, Springer, 2023.
Nguyen, Son H.; Kolbe, Thomas H.: Path-tracing Semantic Networks to Interpret Changes in Semantic 3D City Models. Proceedings of the 17th International 3D GeoInfo Conference 2022 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences), ISPRS, 2022.
Nguyen, Son H.; Kolbe, Thomas H.: Modelling Changes, Stakeholders and their Relations in Semantic 3D City Models. Proceedings of the 16th International 3D GeoInfo Conference 2021 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences), ISPRS, 2021, 137-144.
Nguyen, Son H.; Kolbe, Thomas H.: A Multi-Perspective Approach to Interpreting Spatio-Semantic Changes of Large 3D City Models in CityGML using a Graph Database. Proceedings of the 15th International 3D GeoInfo Conference 2020 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences), ISPRS, 2020, 143–150.
Nguyen, Son H.; Yao, Zhihang; Kolbe, Thomas H.: Spatio-Semantic Comparison of Large 3D City Models in CityGML Using a Graph Database. gis.Science (3/2018), 2018, 85-100.
Nguyen, Son H.; Yao, Zhihang; Kolbe, Thomas H.: Spatio-Semantic Comparison of Large 3D City Models in CityGML Using a Graph Database. Proceedings of the 12th International 3D GeoInfo Conference 2017 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences), ISPRS, 2017, 99-106.
Nguyen, Son H.: Spatio-semantic Comparison of 3D City Models in CityGML using a Graph Database. Master thesis, 2017.
This project is licensed under the MIT License - see the LICENSE file for details.
The development of these methods and implementations were supported and partially funded by the company CADFEM within a dedicated collaboration project in the context of the Leonhard Obermeyer Center (LOC) at the Technical University of Munich (TUM).
If you have any questions or suggestions, please contact:
Son H. Nguyen
Chair of Geoinformatics | Department of Aerospace and Geodesy
TUM School of Engineering and Design | Technical University of Munich (TUM)
Arcisstr. 21, 80333 Munich, Germany
Email: [email protected]