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IJGI
Special Issues
Knowledge-Guided Map Representation and Understanding
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Knowledge-Guided Map Representation and Understanding

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2476

Special Issue Editors

Prof. Dr. Peng Ti

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Guest Editor
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
Interests: cartography; schematic map representation; information visualization
Prof. Dr. Wanzeng Liu

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Guest Editor
National Geomatics Center of China, Beijing, China
Interests: spatial knowledge services; geographic information systems; spatial-temporal knowledge visualization and service
Prof. Dr. Wenhao Yu

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Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: spatio-temporal data mining; map generalization; large language model
Dr. Tian Lan

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Guest Editor
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
Interests: intelligent cartography; geography complexity analysis
Dr. Wende Li

E-Mail Website
Guest Editor
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, China
Interests: map generalization; spatial similarity relation measurement; scale transformation; spatial analysis and spatial information visualization; geographic information engineering

Special Issue Information

Dear Colleagues,

Maps serve as crucial tools for revealing socio-economic and natural phenomena, as well as uncovering cognitive patterns in spatio-temporal distributions. Generating effective map representations based on spatio-temporal and semantic knowledge and enabling automated map understanding are key to enhancing user‑centric map usability. Recent advances in AI, particularly deep learning and large language models (LLMs), have significantly propelled progress toward these goals. 

This Special Issue delves into the mutual construction of knowledge and map representation. One direction examines how spatio-temporal data, semantic structures, cognitive models, and cartographic design knowledge can be transformed into task-oriented map representations. The other investigates the automatic extraction of structured knowledge from these maps to support map design and visualization, spatial reasoning, semantic QA services, and intelligent decision‑making. We welcome interdisciplinary submissions that combine theory, methodology, modeling, and applications across cognitive cartography, representation design, computer vision, knowledge graphs, and multimodal foundation models, aiming to advance the symbiotic evolution of map representation and knowledge service under the influence of intelligent technologies. 

Topics of Interest (include but are not limited to):

  • Knowledge-driven cartography representation;
  • Hybrid-intelligent map design, generation, service, and applications;
  • Cognitive and perceptual foundations of spatial visualization;
  • Automatic extraction of spatial-temporal and cartography knowledge from maps;
  • Automatic map interpretation and understanding;
  • Spatial question answering and reasoning using cartographic sources;
  • Map-based training data for large language or vision-language models;
  • Human–map interaction and visual reasoning;
  • Integration of spatial knowledge and cartographic representations in digital twins.

Prof. Dr. Peng Ti
Prof. Dr. Wanzeng Liu
Prof. Dr. Wenhao Yu
Dr. Tian Lan
Dr. Wende Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • map representation
  • map understanding
  • cartography
  • Artificial Intelligence (AI)
  • spatio-temporal knowledge
  • semantic knowledge
  • knowledge extraction

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Published Papers (2 papers)

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20 pages, 5947 KB  
Article
A Knowledge Graph-Guided and Multimodal Data Fusion-Driven Rapid Modeling Method for Digital Twin Scenes: A Case Study of Bridge Tower Construction
by Yongtao Zhang, Yongwei Wang, Zhihao Guo, Jun Zhu, Fanxu Huang, Hao Zhu, Yuan Chen and Yajian Kang
ISPRS Int. J. Geo-Inf. 2026, 15(1), 27; https://doi.org/10.3390/ijgi15010027 - 6 Jan 2026
Viewed by 446
Abstract
Establishing digital twin scenes facilitates the understanding of geospatial phenomena, representing a significant research focus for GIS scientists and engineers. However, current research on digital twin scenes modeling relies on manual intervention or the overlay of static models, resulting in low modeling efficiency [...] Read more.
Establishing digital twin scenes facilitates the understanding of geospatial phenomena, representing a significant research focus for GIS scientists and engineers. However, current research on digital twin scenes modeling relies on manual intervention or the overlay of static models, resulting in low modeling efficiency and poor standardization. To address these challenges, this paper proposes a knowledge graph-guided and multimodal data fusion-driven rapid modeling method for digital twin scenes, using bridge tower construction as an illustrative example. We first constructed a knowledge graph linking the three domains of “event-object-data” in bridge tower construction. Guided by this graph, we designed a knowledge graph-guided multimodal data association and fusion algorithm. Then a rapid modeling method for bridge tower construction scenes based on dynamic data was established. Finally, a prototype system was developed, and a case study area was selected for analysis. Experimental results show that the knowledge graph we built clearly captures all elements and their relationships in bridge tower construction scenes. Our method enables precise fusion of 5 types of multimodal data: BIM, DEM, images, videos, and point clouds. It improves spatial registration accuracy by 21.83%, increases temporal fusion efficiency by 65.6%, and reduces feature fusion error rates by 70.9%. Local updates of the 3D geographic scene take less than 30 ms, supporting millisecond-level digital twin modeling. This provides a practical reference for building geographic digital twin scenes. Full article
(This article belongs to the Special Issue Knowledge-Guided Map Representation and Understanding)
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18 pages, 1972 KB  
Article
Automatic Reconstruction of 3D Building Models from ALS Point Clouds Based on Façade Geometry
by Tingting Zhao, Tao Xiong, Muzi Li and Zhilin Li
ISPRS Int. J. Geo-Inf. 2025, 14(12), 462; https://doi.org/10.3390/ijgi14120462 - 25 Nov 2025
Viewed by 885
Abstract
Three-dimensional (3D) building models are essential for urban planning, spatial analysis, and virtual simulations. However, most reconstruction methods based on Airborne LiDAR Scanning (ALS) rely primarily on rooftop information, often resulting in distorted footprints and the omission of façade semantics such as windows [...] Read more.
Three-dimensional (3D) building models are essential for urban planning, spatial analysis, and virtual simulations. However, most reconstruction methods based on Airborne LiDAR Scanning (ALS) rely primarily on rooftop information, often resulting in distorted footprints and the omission of façade semantics such as windows and doors. To address these limitations, this study proposes an automatic 3D building reconstruction method driven by façade geometry. The proposed method introduces three key contributions: (1) a façade-guided footprint generation strategy that eliminates geometric distortions associated with roof projection methods; (2) robust detection and reconstruction of façade openings, enabling reliable identification of windows and doors even under sparse ALS conditions; and (3) an integrated volumetric modeling pipeline that produces watertight models with embedded façade details, ensuring both structural accuracy and semantic completeness. Experimental results show that the proposed method achieves geometric deviations at the decimeter level and feature recognition accuracy exceeding 97%. On average, the reconstruction time of a single building is 91 s, demonstrating reliable reconstruction accuracy and satisfactory computational performance. These findings highlight the potential of the method as a robust and scalable solution for large-scale ALS-based urban modeling, offering substantial improvements in both structural precision and semantic richness compared with conventional roof-based approaches. Full article
(This article belongs to the Special Issue Knowledge-Guided Map Representation and Understanding)
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