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

Deadline for manuscript submissions: 25 October 2025 | Viewed by 13528

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Special Issue Editors

Dr. Ivana Ivánová

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Guest Editor

School of Earth and Planetary Sciences, Curtin Perth, Kent Street, Bentley, WA 6102, Australia
Interests: spatial data quality and spatial metadata; provenance of spatial resources; spatial information infrastructures
Special Issues, Collections and Topics in MDPI journals

Dr. Yongze Song

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Guest Editor

School of Design and the Built Environment, Curtin University, Kent Street, Bentley, WA 6102, Australia
Interests: sustainable development; spatial statistics; geospatial methods; urban remote sensing; sustainable infrastructure
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Prof. Dr. Sisi Zlatanova

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Guest Editor

Faculty of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
Interests: 3D indoor modelling; 3D GIS; integration of BIM and GIS; 3D spatial analysis; DBMS; emergency response
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue covers the crucial roles of advanced spatial information and geospatial technologies in improving living spaces' quality and sustainability. Studies on spatial information for improving living spaces require interdisciplinary knowledge and technologies in various fields, such as spatial data representation, artificial intelligence, geo-computation, and digital twins. This topic is crucial for addressing complex challenges related to urban and regional development, public health, and environmental sustainability. Improving living spaces is essential for making well-informed decisions and fostering innovation in the public and private sectors. It also plays a significant role in driving progress, leading to more the growth of livable communities.

This Special Issue will present advanced research and discuss the practical uses of spatial information technology that enhance living spaces. The topic aligns closely with the journal's focus on promoting interdisciplinary studies related to the processing, analysis, and visualization of spatial data. Our objective is to connect theoretical progress with practical implementations for the influence of spatial information on improving living spaces, such as urban and regional environments, public health, and cultural heritage conservation.

The Special Issue welcomes diverse submissions, encompassing original research articles and reviews on various topics such as spatial data interoperability, AI-powered spatial analysis, geo-computation and simulation, digital twins for urban planning, and extended reality in geovisualization. The contributions may also contain novel methodologies in sensor web, Internet of Things (IoT) applications for intelligent environments, spatially enabled health interventions, the visualization of cultural heritage, and the incorporation of spatial information for achieving the sustainable development objectives of promoting progress in spatial science and encouraging its utilization for enhancing living spaces.

This Special Issue will support the ISPRS TC IV Symposium 2024 in Perth, Australia. For more information, use this link: https://www.isprs.org/tc4-symposium2024/index.html.

Dr. Ivana Ivánová
Dr. Yongze Song
Prof. Dr. Sisi Zlatanova
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

spatial data interoperability
artificial intelligence in spatial analysis
geo-computation and geo-simulation
digital twins and urban planning
extended reality in geovisualization
sensor web and IoT for smart environments
spatial information for public health
cultural heritage visualization

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Published Papers (9 papers)

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Research

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25 pages, 6045 KiB

Open AccessArticle

Spatial Analysis of Urban Expansion and Energy Consumption Using Nighttime Light Data: A Comparative Study of Google Earth Engine and Traditional Methods for Improved Living Spaces

by Thidapath Anucharn, Phongsakorn Hongpradit, Niti Iamchuen and Supattra Puttinaovarat

ISPRS Int. J. Geo-Inf. 2025, 14(4), 178; https://doi.org/10.3390/ijgi14040178 - 18 Apr 2025

Viewed by 236

Abstract

This study employs a dual methodological approach, integrating Google Earth Engine (GEE) and unsupervised classification (UNSUP) to analyze urban expansion patterns in Chiang Mai province using nighttime light imagery. The research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data from 2014 to 2023 to assess urban growth dynamics. The primary objectives are to (1) evaluate the performance of GEE and UNSUP in nighttime light data processing, (2) validate urban area classification accuracy using multiple assessment metrics, and (3) examine the relationship between nighttime light intensity and electricity consumption through Pearson’s correlation analysis, thereby establishing urban growth patterns. The methodological framework incorporates a dual-threshold classification mechanism in GEE and K-means clustering in traditional geospatial software. Accuracy assessment is conducted using 256 stratified random sampling points, complemented by land use and land cover (LULC) data for ground truth validation. The results indicate that GEE consistently outperforms UNSUP, achieving overall accuracy values between 0.80 and 0.82, compared to 0.73 and 0.76 for UNSUP. The Kappa coefficient for GEE ranges from 0.60 to 0.65, whereas UNSUP demonstrates lower agreement with ground truth data (0.44–0.52). Furthermore, both approaches reveal a significant correlation between electricity consumption and nighttime light intensity, with R² = 0.9744 for GEE and R² = 0.9759 for UNSUP, confirming the efficacy of nocturnal illumination data in urban expansion monitoring. The findings indicate that urban areas in Chiang Mai have expanded by approximately 70% over the study period. This research contributes to the field by demonstrating the effectiveness of integrated geospatial methodologies in urban development analysis. The findings offer urban planners and policymakers critical insights for sustainable urban growth management and decision-making. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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30 pages, 16455 KiB

Open AccessArticle

Automated Detection of Pedestrian and Bicycle Lanes from High-Resolution Aerial Images by Integrating Image Processing and Artificial Intelligence (AI) Techniques

by Richard Boadu Antwi, Prince Lartey Lawson, Michael Kimollo, Eren Erman Ozguven, Ren Moses, Maxim A. Dulebenets and Thobias Sando

ISPRS Int. J. Geo-Inf. 2025, 14(4), 135; https://doi.org/10.3390/ijgi14040135 - 23 Mar 2025

Viewed by 362

Abstract

The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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36 pages, 10042 KiB

Open AccessArticle

Unraveling Spatial Nonstationary and Nonlinear Dynamics in Life Satisfaction: Integrating Geospatial Analysis of Community Built Environment and Resident Perception via MGWR, GBDT, and XGBoost

by Di Yang, Qiujie Lin, Haoran Li, Jinliu Chen, Hong Ni, Pengcheng Li, Ying Hu and Haoqi Wang

ISPRS Int. J. Geo-Inf. 2025, 14(3), 131; https://doi.org/10.3390/ijgi14030131 - 20 Mar 2025

Cited by 1 | Viewed by 426

Abstract

Rapid urbanization has accelerated the transformation of community dynamics, highlighting the critical need to understand the interplay between subjective perceptions and objective built environments in shaping life satisfaction for sustainable urban development. Existing studies predominantly focus on linear relationships between isolated factors, neglecting spatial heterogeneity and nonlinear dynamics, which limits the ability to address localized urban challenges. This study addresses these gaps by utilizing multi-scale geographically weighted regression (MGWR) to assess the spatial nonstationarity of subject perceptions and built environment factors while employing gradient-boosting decision trees (GBDT) to capture their nonlinear relationships and incorporating eXtreme Gradient Boosting (XGBoost) to improve predictive accuracy. Using geospatial data (POIs, social media data) and survey responses in Suzhou, China, the findings reveal that (1) proximity to business facilities (β = 0.41) and educational resources (β = 0.32) strongly correlate with satisfaction, while landscape quality shows contradictory effects between central (β = 0.12) and peripheral zones (β = −0.09). (2) XGBoost further quantifies predictive disparities: subjective factors like property service satisfaction (R² = 0.64, MAPE = 3.72) outperform objective metrics (e.g., dining facilities, R² = 0.36), yet objective housing prices demonstrate greater stability (MAPE = 3.11 vs. subjective MAPE = 6.89). (3) Nonlinear thresholds are identified for household income and green space coverage (>15%, saturation effects). These findings expose critical mismatches—residents prioritize localized services over citywide economic metrics, while objective amenities like healthcare accessibility (threshold = 1 km) require spatial recalibration. By bridging spatial nonstationarity (MGWR) and nonlinearity (XGBoost), this study advances a dual-path framework for adaptive urban governance, the community-level prioritization of high-impact subjective factors (e.g., service quality), and data-driven spatial planning informed by nonlinear thresholds (e.g., facility density). The results offer actionable pathways to align smart urban development with socio-spatial equity, emphasizing the need for hyperlocal, perception-sensitive regeneration strategies. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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21 pages, 2540 KiB

Open AccessArticle

The Influence of the Relationship Between Landmark Symbol Types, Annotations, and Colors on Search Performance in Mobile Maps Based on Eye Tracking

by Hao Fang, Hongyun Guo, Zhangtong Song, Nai Yang, Rui Wang and Fen Guo

ISPRS Int. J. Geo-Inf. 2025, 14(3), 129; https://doi.org/10.3390/ijgi14030129 - 14 Mar 2025

Viewed by 516

Abstract

Mobile map landmark symbols are pivotal in conveying spatial semantics and enhancing users’ perception of digital maps. This study employs a three-factor hybrid experimental design to investigate the effects of different landmark symbol types and their color associations with annotations on search performance using eye tracking methods. Utilizing the Tobii X2-60 eye tracker, 40 participants engaged in a visual search task across three symbol types (icons, indexes, and symbols) and two color conditions (consistent and inconsistent). This study also examines the impact of gender on search performance. The results indicate that INDEX, emphasizing the landmarks’ functions and key features, most effectively improve search accuracy and efficiency while demanding the least cognitive effort. In contrast, SYMBOL type characters, with clear semantics and minimal information, require less visual attention, facilitating faster preliminary processing. Additionally, cognitive style differences between genders affect these symbols’ effectiveness in visual searches. A careful selection of symbol types and color combinations can significantly enhance user interaction with mobile maps. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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17 pages, 4391 KiB

Open AccessArticle

OpenStreetMap as the Data Source for Territorial Innovation Potential Assessment

by Otakar Čerba

ISPRS Int. J. Geo-Inf. 2025, 14(3), 127; https://doi.org/10.3390/ijgi14030127 - 12 Mar 2025

Viewed by 651

Abstract

This study explores a methodology for assessing territorial innovation potential using OpenStreetMap (OSM) data and geoinformation technologies. Traditional assessment methods often rely on aggregated statistical data, which provide a generalized view but overlook the spatial heterogeneity within regions. To address this limitation, the proposed methodology utilizes open, up-to-date OSM data to identify key infrastructure elements, such as universities, research institutions, and data centers, which drive regional innovation. The methodology includes data extraction, harmonization, and spatial analysis using tools like QGIS and kernel density estimation. Results from the PoliRuralPlus project pilot regions highlight significant differences in innovation potential between urban centers and rural areas, emphasizing the importance of detailed spatial data in policy making and regional development planning. The study concludes that OSM-based assessments provide spatially detailed targeted, flexible, and replicable insights into regional innovation potential compared to traditional methods. However, the limitations of crowdsourced data, such as variability in quality and completeness, are acknowledged. Future developments aim to integrate OSM with official statistical data and other data resources to support more efficient and fair resource allocation and strategic investments in regional innovation ecosystems. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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26 pages, 6664 KiB

Open AccessArticle

Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China

by Yihang Xiao, Cunzhi Li, Zhiwu Zhou, Dongyang Hou and Xiaoguang Zhou

ISPRS Int. J. Geo-Inf. 2025, 14(1), 23; https://doi.org/10.3390/ijgi14010023 - 9 Jan 2025

Viewed by 929

Abstract

As a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns of urban commercial spaces is essential for improving the efficiency of urban spatial allocation and achieving scientific spatial planning and governance. This paper utilizes multisource spatiotemporal data, employing geographic spatial analysis methods and graph neural network models to explore the spatial structure of commercial service facilities in Beijing and their relationships with population density and land use, thereby achieving a detailed classification of the commercial service patterns at the natural neighborhood scale. The research findings indicate a significant association between commercial service facilities and population, as well as land use, with a strong spatial heterogeneity. There exists a dissonance between the layout of commercial service facilities and population distribution, and the differences in commercial service development across various regions pose challenges to balanced urban development. Based on this, this paper provides specific recommendations for optimizing the urban commercial spatial structure, offering reference points for future urban planning and development. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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19 pages, 12108 KiB

Open AccessArticle

WC-CP: A Bluetooth Low Energy Indoor Positioning Method Based on the Weighted Centroid of the Convex Polygon

by Jinjin Yan, Manyu Zhang, Jinquan Yang, Lyudmila Mihaylova, Weijie Yuan and You Li

ISPRS Int. J. Geo-Inf. 2024, 13(10), 354; https://doi.org/10.3390/ijgi13100354 - 6 Oct 2024

Viewed by 3738

Abstract

Indoor navigation has attracted significant attention from both academic and industrial perspectives. Indoor positioning is a critical component of indoor navigation. Several solutions or technologies have been proposed, such as Wi-Fi, UWB, and Bluetooth. Among them, Bluetooth Low Energy (BLE) is cost-effective, easily deployable, flexible, and efficient. This paper focuses on indoor positioning solely based on BLE. Motivated by two observations, namely, that (i) involving more anchor nodes can enhance positioning accuracy, and that (ii) narrowing the area for unknown location determination can also lead to improved accuracy, a new distance-based method, the Weighted Centroid of the Convex Polygon (WC-CP), is proposed. While it is generally acknowledged that incorporating more anchor nodes can enhance indoor positioning performance, the current state of the art lacks a robust methodology for selecting and utilizing these nodes. The WC-CP approach addresses this gap by introducing a systematic and efficient method for identifying and employing the most suitable anchor nodes. By avoiding nodes that could potentially introduce significant errors or lead to incorrect localization, our method ensures more accurate and reliable indoor positioning. The efficacy of WC-CP is demonstrated in an indoor environment, achieving an RMSE of 1.35 m. This result shows significant improvements over three state-of-the-art approaches, about 34.15% better than LSBM, 32.50% better than TWCBM, and 30.05% better than ITWCBM. These findings underscore the potential of WC-CP for enhanced accuracy and reliability in indoor positioning based on BLE. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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29 pages, 19449 KiB

Open AccessArticle

Influencing Factors of Street Vitality in Historic Districts Based on Multisource Data: Evidence from China

by Bing Yu, Jing Sun, Zhaoxing Wang and Sanfeng Jin

ISPRS Int. J. Geo-Inf. 2024, 13(8), 277; https://doi.org/10.3390/ijgi13080277 - 5 Aug 2024

Cited by 6 | Viewed by 2314

Abstract

Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi’an’s historical and cultural district as examples, this research utilizes multisource data, including points of interest (POIs), street view images, and Baidu heatmaps, alongside analytical techniques such as machine learning. This study explores the determinants of street vitality from the dual perspectives of its external manifestation and spatial carriers. A quantitative framework for measuring street vitality in historic districts is established, thoroughly examining the driving factors behind street vitality. Additionally, the relationship between built environment indicators and street vitality is elucidated through statistical analysis methods. The findings reveal significant, time-varying influences of these spatial carriers on human vitality, with distinct spatial distribution patterns of human activity across different times, and the significance of the influence of external representations of human vitality and various types of spatial carriers varies over time. Based on these insights, this paper proposes strategies for enhancing the vitality of historic streets, aiming to rejuvenate and sustain the diverse and dynamic energy of these districts. It provides a foundation for revitalizing the vigor of cultural heritage zones and offers strategies applicable to similar urban contexts. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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Review

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19 pages, 290 KiB

Open AccessReview

Urban Vitality Measurement Through Big Data and Internet of Things Technologies

by Young-Long Kim

ISPRS Int. J. Geo-Inf. 2025, 14(1), 14; https://doi.org/10.3390/ijgi14010014 - 2 Jan 2025

Cited by 2 | Viewed by 1186

Abstract

This paper examines the evolution of urban vitality measurement, emphasizing the transformative impact of big data and Internet of Things (IoT) technologies. Traditionally assessed through direct observations and surveys, urban vitality measurement has shifted with the advent of these technologies, enabling the collection of vast amounts of urban data. This approach offers a more dynamic and comprehensive picture of urban vitality, facilitated by advanced analytical tools such as machine learning and predictive analytics, which can interpret complex datasets to offer real-time insights and better decision-making for urban planning. However, this shift also raises significant methodological and ethical concerns, particularly regarding privacy, reliability, and accuracy. The paper discusses the theoretical underpinnings of urban vitality, current technological advancements, and the challenges and future directions in urban studies. It highlights the need for an interdisciplinary approach to fully harness the potential of emerging technologies in developing livable, sustainable, and responsive cities. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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