2 Search Results for "Fan, Hongchao"

Document
DOI: 10.4230/LIPIcs.GIScience.2025.2
CityJSON Management Using Multi-Model Graph Database to Support 3D Urban Data Management

Authors: Muhammad Syafiq, Suhaibah Azri, and Uznir Ujang

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
The prevalence of 3D city models in urban applications is increasing due to their lightweight and flexibility, making them adaptable to various applications. However, effective data interoperability remains an issue. Managing 3D city models within a database can improve urban data management applications such as data enrichment and efficient querying. Motivated by the need for better interoperability of 3D city models, this paper proposes a novel method for storing CityJSON using the concept of a multi-model graph database as a foundation for enriching its semantics. The proposed approach involves decomposing CityJSON objects into smaller JSON components, which are then abstracted into graph elements. Parent-child and other relationship attributes are modelled to capture the hierarchical and associative structures of the CityJSON data. A specific programme is employed to preprocess CityJSON data based on several conditions before being loaded into the graph database. Our multi-model approach allows three types of queries: document, graph, and hybrid. The latter combines both document and graph query. Comparative evaluation against relational databases demonstrates that the proposed method outperforms in terms of query performance. The improved query performance is attributed to the advantage of graph database that reduces the need for joins and the ability to efficiently index and navigate JSON data. The findings of this study establish a foundation for semantic enrichment of 3D city models to improve interoperability and support advanced urban data management.
Cite as

Muhammad Syafiq, Suhaibah Azri, and Uznir Ujang. CityJSON Management Using Multi-Model Graph Database to Support 3D Urban Data Management. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{syafiq_et_al:LIPIcs.GIScience.2025.2,
  author =	{Syafiq, Muhammad and Azri, Suhaibah and Ujang, Uznir},
  title =	{{CityJSON Management Using Multi-Model Graph Database to Support 3D Urban Data Management}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{2:1--2:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.2},
  URN =		{urn:nbn:de:0030-drops-238310},
  doi =		{10.4230/LIPIcs.GIScience.2025.2},
  annote =	{Keywords: CityJSON, Graph Database, 3D City Model, 3D GIS, Interoperability}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.7
Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Authors: Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan, Alexander Zipf, and Martin Werner

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability.
Cite as

Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan, Alexander Zipf, and Martin Werner. Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{li_et_al:LIPIcs.GIScience.2023.7,
  author =	{Li, Hao and Yuan, Zhendong and Dax, Gabriel and Kong, Gefei and Fan, Hongchao and Zipf, Alexander and Werner, Martin},
  title =	{{Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{7:1--7:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.7},
  URN =		{urn:nbn:de:0030-drops-189028},
  doi =		{10.4230/LIPIcs.GIScience.2023.7},
  annote =	{Keywords: OpenStreetMap, Street-view Images, VGI, GeoAI, 3D city model, Facade parsing}
}
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