4 Search Results for "Ding, Linfang"

Document
DOI: 10.4230/LIPIcs.GIScience.2025.13
Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation

Authors: Eftychia Koukouraki, Auriol Degbelo, and Christian Kray

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

Abstract
Reproducibility is a key principle of the modern scientific method. Maps, as an important means of communicating scientific results in GIScience and across disciplines, should be reproducible. Currently, map reproducibility assessment is done manually, which makes the assessment process tedious and time-consuming, ultimately limiting its efficiency. Hence, this work explores the extent to which Visual Question-Answering (VQA) can be used to automate some tasks relevant to map reproducibility assessment. We selected five state-of-the-art vision language models (VLMs) and followed a three-step approach to evaluate their ability to discriminate between maps and other images, interpret map content, and compare two map images using VQA. Our results show that current VLMs already possess map-reading capabilities and demonstrate understanding of spatial concepts, such as cardinal directions, geographic scope, and legend interpretation. Our paper demonstrates the potential of using VQA to support reproducibility assessment and highlights the outstanding issues that need to be addressed to achieve accurate, trustworthy map descriptions, thereby reducing the time and effort required by human evaluators.
Cite as

Eftychia Koukouraki, Auriol Degbelo, and Christian Kray. Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 13:1-13:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koukouraki_et_al:LIPIcs.GIScience.2025.13,
  author =	{Koukouraki, Eftychia and Degbelo, Auriol and Kray, Christian},
  title =	{{Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{13:1--13:12},
  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.2025.13},
  URN =		{urn:nbn:de:0030-drops-238426},
  doi =		{10.4230/LIPIcs.GIScience.2025.13},
  annote =	{Keywords: map comparison, computational reproducibility, visual question answering, large language models, GeoAI}
}
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
Survey
DOI: 10.4230/TGDK.2.1.4
Logics for Conceptual Data Modelling: A Review

Authors: Pablo R. Fillottrani and C. Maria Keet

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1

Abstract
Information modelling for databases and object-oriented information systems avails of conceptual data modelling languages such as EER and UML Class Diagrams. Many attempts exist to add logical rigour to them, for various reasons and with disparate strengths. In this paper we aim to provide a structured overview of the many efforts. We focus on aims, approaches to the formalisation, including key dimensions of choice points, popular logics used, and the main relevant reasoning services. We close with current challenges and research directions.
Cite as

Pablo R. Fillottrani and C. Maria Keet. Logics for Conceptual Data Modelling: A Review. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 4:1-4:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{fillottrani_et_al:TGDK.2.1.4,
  author =	{Fillottrani, Pablo R. and Keet, C. Maria},
  title =	{{Logics for Conceptual Data Modelling: A Review}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:30},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.4},
  URN =		{urn:nbn:de:0030-drops-198616},
  doi =		{10.4230/TGDK.2.1.4},
  annote =	{Keywords: Conceptual Data Modelling, EER, UML, Description Logics, OWL}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.28
GeoQAMap - Geographic Question Answering with Maps Leveraging LLM and Open Knowledge Base (Short Paper)

Authors: Yu Feng, Linfang Ding, and Guohui Xiao

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

Abstract
GeoQA (Geographic Question Answering) is an emerging research field in GIScience, aimed at answering geographic questions in natural language. However, developing systems that seamlessly integrate structured geospatial data with unstructured natural language queries remains challenging. Recent advancements in Large Language Models (LLMs) have facilitated the application of natural language processing in various tasks. To achieve this goal, this study introduces GeoQAMap, a system that first translates natural language questions into SPARQL queries, then retrieves geospatial information from Wikidata, and finally generates interactive maps as visual answers. The system exhibits great potential for integration with other geospatial data sources such as OpenStreetMap and CityGML, enabling complicated geographic question answering involving further spatial operations.
Cite as

Yu Feng, Linfang Ding, and Guohui Xiao. GeoQAMap - Geographic Question Answering with Maps Leveraging LLM and Open Knowledge Base (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 28:1-28:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{feng_et_al:LIPIcs.GIScience.2023.28,
  author =	{Feng, Yu and Ding, Linfang and Xiao, Guohui},
  title =	{{GeoQAMap - Geographic Question Answering with Maps Leveraging LLM and Open Knowledge Base}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{28:1--28:7},
  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.2023.28},
  URN =		{urn:nbn:de:0030-drops-189233},
  doi =		{10.4230/LIPIcs.GIScience.2023.28},
  annote =	{Keywords: Geographic Question Answering, Large Language Models, SPARQL, Knowledge Base, Wikidata}
}
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