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IJGI
Special Issues
Indoor Mobile Mapping and Location-Based Knowledge Services
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Indoor Mobile Mapping and Location-Based Knowledge Services

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2277

Special Issue Editors

Prof. Dr. Eliseo Clementini

E-Mail Website
Guest Editor
Department of Industrial and Information Engineering and Economics, University of L'Aquila, 67100 L'Aquila, Italy
Interests: spatial databases; spatial query languages; mathematical modeling of spatial information; computational geometry; spatio-temporal reasoning; qualitative modeling of geographical information; indoor and outdoor navigation; volunteered geographic information
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiyong Zhou

E-Mail Website
Guest Editor
Department of Geography, University of Zurich, Zurich, Switzerland
Interests: indoor and outdoor mobility analytics; geospatial data engineering; geospatial information communication; GeoAI applications for sustainable cities

Special Issue Information

Dear Colleagues,

The ability to accurately map and navigate indoor environments is a cornerstone of modern spatial technologies, driving innovation in diverse fields such as smart cities, healthcare, logistics, and augmented reality. Unlike outdoor environments, where GPS and satellite imaging dominate, indoor spaces present unique challenges: signal occlusion, a lack of global positioning system (GPS) coverage, and complex spatial structures. These challenges have spurred the development of advanced indoor mobile mapping technologies and location-based knowledge services that leverage novel sensors, algorithms, and data processing techniques.

Indoor mobile mapping combines tools like LiDAR, vision systems, and sensor fusion to create high-resolution, three-dimensional representations of spaces. Paired with sophisticated localization methods such as Wi-Fi fingerprinting, ultra-wideband (UWB) tracking, or visual-inertial odometry; these systems enable precise navigation and spatial analysis within buildings. As the amount of spatial data increases, the focus is shifting from mere data collection to actionable insights, with location-based knowledge services playing a pivotal role in transforming raw spatial information into user-centric solutions for navigation, resource optimization, and decision-making.

The importance of this research area is underscored by its applicability across numerous industries:

  • Smart Cities and Infrastructure: Enabling efficient building management and energy optimization.
  • Retail and Logistics: Facilitating real-time inventory tracking and customer navigation.
  • Healthcare: Supporting indoor navigation in hospitals and resource allocation during emergencies.
  • Emergency Response: Enhancing rescue operations with real-time indoor positioning.

This Special Issue aims to achieve the following:

  1. Highlight Advances in Technology: Showcase the latest breakthroughs in indoor mapping and localization technologies, including sensor development, algorithmic innovations, and system integrations.
  2. Foster Interdisciplinary Research: Bring together expertise from geospatial science, computer vision, robotics, and data analytics to address the multifaceted challenges of indoor environments.
  3. Promote Applications and Case Studies: Provide a platform for real-world implementations of indoor mapping and location-based knowledge services, demonstrating their value across various sectors.
  4. Explore Standards and Ethical Considerations: Address the need for interoperability, accuracy benchmarks, and privacy-preserving methodologies to guide the responsible development of these technologies.
  5. Define Future Directions: Identify research gaps and emerging opportunities to inspire innovation and collaboration within this growing field.

Relevance to the Journal’s Scope

The subject of this Special Issue aligns closely with the journal’s focus on advancing spatial sciences, technology development, and applications. Indoor mobile mapping and location-based knowledge services embody the interdisciplinary nature of the journal’s scope by integrating geospatial technologies, data science, and practical applications. Furthermore, the topics proposed resonate with the journal's commitment to fostering innovation that benefits society and supports sustainable, efficient, and intelligent systems.

Themes for Exploration

This Special Issue would like to invite contributions on the following themes:

  • Advanced sensors and algorithms for indoor mapping.
  • AI-driven approaches for indoor localization and mapping.
  • Integration of indoor and outdoor mapping systems.
  • Applications of AR/VR in location-based knowledge services.
  • Standards and benchmarks for indoor spatial data.
  • Privacy-preserving solutions for indoor LBS.
  • Case studies on successful deployments in diverse industries.
  • Evacuation simulation with real-time positioning.

By addressing these motivations and themes, this Special Issue aims to provide a comprehensive understanding of current advancements, challenges, and future opportunities in indoor mobile mapping and location-based knowledge services, setting the stage for impactful research and innovation.

Prof. Dr. Eliseo Clementini
Dr. Zhiyong Zhou
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • indoor mapping technologies
  • location-based knowledge services
  • indoor navigation systems
  • geospatial data for indoor spaces
  • real-time positioning and tracking
  • AI-driven indoor location services
  • indoor spatial analytics
  • smart indoor environments

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 3522 KiB  
Article
PCA-GWO-KELM Optimization Gait Recognition Indoor Fusion Localization Method
by Xiaoyu Ji, Xiaoyue Xu, Suqing Yan, Jianming Xiao, Qiang Fu and Kamarul Hawari Bin Ghazali
ISPRS Int. J. Geo-Inf. 2025, 14(7), 246; https://doi.org/10.3390/ijgi14070246 - 26 Jun 2025
Viewed by 248
Abstract
Location-based services have important economic and social values. The positioning accuracy and cost have a crucial impact on the quality, promotion, and market competitiveness of location services. Dead reckoning can provide accurate location information in a short time. However, it suffers from motion [...] Read more.
Location-based services have important economic and social values. The positioning accuracy and cost have a crucial impact on the quality, promotion, and market competitiveness of location services. Dead reckoning can provide accurate location information in a short time. However, it suffers from motion pattern diversity and cumulative error. To address these issues, we propose a PCA-GWO-KELM optimization gait recognition indoor fusion localization method. In this method, 30-dimensional motion features for different motion patterns are extracted from inertial measurement units. Then, constructing PCA-GWO-KELM optimization gait recognition algorithms to obtain important features, the model parameters of the kernel-limit learning machine are optimized by the gray wolf optimization algorithm. Meanwhile, adaptive upper thresholds and adaptive dynamic time thresholds are constructed to void pseudo peaks and valleys. Finally, fusion localization is achieved by combining with acoustic localization. Comprehensive experiments have been conducted using different devices in two different scenarios. Experimental results demonstrated that the proposed method can effectively recognize motion patterns and mitigate cumulative error. It achieves higher localization performance and universality than state-of-the-art methods. Full article
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
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25 pages, 9860 KiB  
Article
Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering
by Yiming Li, Luying Na, Xianpu Liang and Qi An
ISPRS Int. J. Geo-Inf. 2025, 14(7), 236; https://doi.org/10.3390/ijgi14070236 - 21 Jun 2025
Viewed by 530
Abstract
To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using [...] Read more.
To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using the YOLOv8 model, and geometric constraints between prior static objects and dynamic regions are introduced to identify non-prior dynamic objects, thereby eliminating all dynamic features (both prior and non-prior). Second, an initial semantic point cloud map is constructed by integrating prior static features from a semantic segmentation network with pose estimates from an RGB-D camera. Dynamic noise is then removed using statistical outlier removal (SOR) filtering, while voxel filtering optimizes point cloud density, generating a compact yet texture-rich semantic dense point cloud map with minimal dynamic artifacts. Subsequently, a multi-resolution semantic octree map is built using a recursive spatial partitioning algorithm. Finally, point cloud poses are corrected via Transform Frame (TF) transformation, and a 2D traversability grid map is generated using passthrough filtering and grid projection. Experimental results demonstrate that the proposed method constructs multi-level semantic maps with rich information, clear structure, and high reliability in indoor dynamic scenarios. Additionally, the map file size is compressed by 50–80%, significantly enhancing the reliability of mobile robot navigation and the efficiency of path planning. Full article
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
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21 pages, 6514 KiB  
Article
Evacuation Behavioural Instructions with 3D Motions: Insights from Three Use Cases
by Ruihang Xie, Sisi Zlatanova, Jinwoo (Brian) Lee and André Borrmann
ISPRS Int. J. Geo-Inf. 2025, 14(5), 197; https://doi.org/10.3390/ijgi14050197 - 8 May 2025
Viewed by 828
Abstract
During emergency evacuations, pedestrians may use three-dimensional (3D) motions, such as low crawling and climbing up/down, to navigate above or below indoor objects (e.g., tables, chairs, and stair flights). Understanding how these motions influence evacuation processes can facilitate the development of behavioural instructions. [...] Read more.
During emergency evacuations, pedestrians may use three-dimensional (3D) motions, such as low crawling and climbing up/down, to navigate above or below indoor objects (e.g., tables, chairs, and stair flights). Understanding how these motions influence evacuation processes can facilitate the development of behavioural instructions. This study examines the influence of 3D motions through a simulation-based method. This method combines a voxel-based 3D indoor model with an agent-based model. Three use case studies are elaborated upon, considering varying building types, agent numbers, urgency levels, and demographic differences. These case studies serve as exploratory demonstrations rather than validated simulations grounded in real-world evacuation experiments. Our findings are as follows: (1) Three-dimensional motions may create alternative and local 3D paths, enabling agents to bypass congestion, particularly in narrow corridors and confined spaces. (2) While 3D motions may help alleviate local congestion, they may intensify bottlenecks near exits, especially in highly crowded and high-urgency scenarios. (3) As urgency and agent numbers increase, differences in evacuation efficiency between scenarios with and without 3D motions are likely to diminish. We suggest further investigation into evacuation behavioural instructions, including the following: (1) conditional use of 3D motions in different buildings and (2) instructions tailored to different demographic groups. These use cases illustrate new directions for evacuation managers to consider the incorporation of 3D motions. Full article
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
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