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Novel Theories and Applications on Geo-Spatial Databases, Models and AI...
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Novel Theories and Applications on Geo-Spatial Databases, Models and AI in Urban Science, Planning, Development and Governance

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2130

Editors

Prof. Dr. Guoqiang Shen

E-Mail Website
Guest Editor
Department of Regional and City Planning, Zhejiang University, Hangzhou, China
Interests: urban and regional planning; research methods; GIS applications
Special Issues, Collections and Topics in MDPI journals
Dr. Qiuxiao Chen

E-Mail Website
Guest Editor
College of Spatial Planning and Design, Hangzhou City College, Hangzhou, China
Interests: GIS; remote sensing; spatial planning; spatial analysis of smart and resilient cities and regions
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yanguang Chen

E-Mail Website
Guest Editor
College of Urban and Environmental Sciences, Peking University, Beijing, China
Interests: geographical research; complexity and fractal geometry; mathematical methods in urban spatial modeling; smart cities and regions
Special Issues, Collections and Topics in MDPI journals
Dr. Long Zhou

E-Mail Website
Guest Editor
Faculty of Innovation and Design, City University of Macau, Macau, China
Interests: urban and regional planning; landscape planning; ecosystems services; coastal and resillient cities; Macau studies
Dr. Yu Liu

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Henan University, Kaifeng, China
Interests: GIS; remote-sensing spatial analysis; regional and city planning; environmental and land-use planning
Dr. Xindong He

E-Mail Website
Guest Editor
College of Geography and Planning, Chengdu Polytechnic University, Chengdu, China
Interests: regional and city planning; spatial analysis and spatial statistics; GIS and RS; new methods in planning

Special Issue Information

Dear Colleagues,

Urban science, planning, development and governance have become increasingly important for smart, sustainable and resilient cities around the world, especially since over a half of the world population now live and work in cities, with developed countries such as Japan, America and European nations having urbanization rates of over 85%. Large and rapidly developing nations such as China, India, Indonesia, Argentina and Brazil have seen their urban population explode over the past 50 years. Livability, safety, mobility, viability, sustainability and resilience have become increasingly critical research and policy issues cities face their urban planning, development and governance. Advanced geo-spatial technologies such as sensors, big data and modeling tools, coupled with the latest AI, have become popular in both urban science research and applications.

The aim of this proposed Special Issue is to solicitate the latest cutting-edge technical and modeling studies on novel geo-spatial theories and applications in the areas of urban science, urban planning, urban administration and urban development.

  • Theme:

Novel theories and applications on geo-spatial databases, models and AI in urban science, planning, development and governance.

  • Type：

(a) Full-length articles (10000+/- words), (b) Short technical notes or concept papers less than 5000 words.

Prof. Dr. Guoqiang Shen
Dr. Qiuxiao Chen
Prof. Dr. Yanguang Chen
Dr. Long Zhou
Dr. Yu Liu
Dr. Xindong He
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-anonymized 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

  • geographical information system
  • urban and regional planning
  • geo-design and digital twins
  • databases, models and visualization
  • geospatial artificial intelligence
  • resilient and healthy cities
  • sustainable and smart cities
  • land covers and land uses
  • social and economic development
  • urban governance and management
  • urban spatial complexity and intelligence
  • urban analytics and algorithms
  • human behaviors in cities
  • urban policy and politics

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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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

30 pages, 4494 KB  
Article
From Spatial Evolution to Low-Carbon Transition: Regional Heterogeneity and Stage Diagnosis of Carbon Emissions Across 19 Urban Agglomerations in China
by Ye Duan, Minghan Yang, Zhaowei Hou, Hongye Wang, Albert Fekete and Dongge Ning
ISPRS Int. J. Geo-Inf. 2026, 15(8), 352; https://doi.org/10.3390/ijgi15080352 - 4 Aug 2026
Viewed by 282
Abstract
Understanding the spatiotemporal dynamics of carbon emissions and developing differentiated governance strategies for urban agglomerations are essential for achieving regional low-carbon transformation. This study aims to identify the spatiotemporal patterns, driving mechanisms, and development-stage differences of carbon emissions across China’s urban agglomerations and [...] Read more.
Understanding the spatiotemporal dynamics of carbon emissions and developing differentiated governance strategies for urban agglomerations are essential for achieving regional low-carbon transformation. This study aims to identify the spatiotemporal patterns, driving mechanisms, and development-stage differences of carbon emissions across China’s urban agglomerations and to establish a type-specific governance framework. Based on multi-source geospatial and socioeconomic data from 19 urban agglomerations for the period 2006–2023, this study integrates spatial autocorrelation analysis, standard deviation ellipse analysis, hotspot analysis, random forest regression with SHAP interpretation, K-medoid clustering, and the Environmental Kuznets Curve (EKC) model to systematically examine emission evolution, influencing factors, and governance pathways. The results indicate the following: (1) carbon emissions in China’s urban agglomerations increased continuously during the study period and exhibited significant spatial heterogeneity, characterized by a “high east–low west” pattern, expanding eastern emission hotspots, and a gradual southwest shift in the emission centroid; (2) industrial structure and economic development level were identified as the dominant factors associated with carbon-emission differences, while energy efficiency, urbanization, and population density showed heterogeneous relationships across regions; (3) five carbon-emission development types were identified, including high-carbon high-development, transition-pressure, resource-dependent, stable-development, and low-carbon potential agglomerations, each exhibiting distinct development characteristics and governance requirements; and (4) EKC analysis revealed differentiated development stages among these types, suggesting that carbon governance should be tailored according to regional development conditions, dominant drivers, and emission-transition stages. This study provides an integrated geospatial modeling framework for understanding carbon-emission heterogeneity and offers scientific support for differentiated low-carbon planning and collaborative governance of urban agglomerations. Full article
(This article belongs to the Special Issue Novel Theories and Applications on Geo-Spatial Databases, Models and AI in Urban Science, Planning, Development and Governance)
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26 pages, 20725 KB  
Article
Channel Attention-Based Multi-Domain Feature Alignment for Moving Vehicle Detection in Satellite Videos Toward Smart Urban Planning
by Ning Zhao, Xiao Wang, Xiaopeng Zhang, Jun Shi, Zhiguo Jiang and Haopeng Zhang
ISPRS Int. J. Geo-Inf. 2026, 15(8), 342; https://doi.org/10.3390/ijgi15080342 - 26 Jul 2026
Viewed by 399
Abstract
Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is [...] Read more.
Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is essential for traffic flow analysis, infrastructure assessment, and dynamic urban planning. Moving vehicle detection in satellite video sequences is a basic task that turns raw imagery into useful traffic-state information, supporting these applications. Despite the advantages of satellite video data, detecting moving vehicles in practice remains a tough problem. Objects are extremely small and lack clear appearance details, while low local contrast makes them hard to separate from complex backgrounds. Satellite platform motion also introduces background misalignment and intensity fluctuations, resulting in missed detections and false alarms that hurt monitoring reliability. Furthermore, current methods do not fully exploit temporal motion cues or transform-domain priors, creating a performance bottleneck that restricts their practical use. To solve these problems, this paper proposes a Channel-Attentive Spatio-Temporal-Frequency Alignment (CASTFA) framework to effectively use and combine multi-dimensional features for moving vehicle detection in satellite videos, with the goal of providing high-quality traffic monitoring data to help smart city planning. Specifically, a State Space-Guided Temporal Compression (SSGTC) module first collects information along the time dimension with linear computational complexity, greatly reducing overhead while keeping motion cues that are critical for traffic-state estimation. The compressed temporal features are then processed with a multi-scale Haar wavelet transform to get hierarchical time-frequency representations that capture subtle motion dynamics across different frequency bands. At the same time, a pre-trained backbone network extracts multi-scale spatial features. To allow these different domains to work together, a Cross-Domain Feature Alignment (CDFA) mechanism aligns and combines spatial and time-frequency features through channel-attentive operations. Experimental results on the publicly available satellite video moving vehicle detection dataset show that the proposed CASTFA method consistently outperforms existing approaches, with better precision, recall, and F1-scores across diverse urban scenarios. These results show that CASTFA can provide reliable moving vehicle detection performance under difficult real-world conditions, supporting accurate traffic-flow monitoring and providing valuable geospatial intelligence for smart urban planning, transportation management, and sustainable city development. Full article
(This article belongs to the Special Issue Novel Theories and Applications on Geo-Spatial Databases, Models and AI in Urban Science, Planning, Development and Governance)
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21 pages, 5389 KB  
Article
The Link Between Urban Resilience and Sustainable Development: Research Trends in Global Nature-Based Solutions Based on Bibliometric Analysis Using CiteSpace and VOSviewer
by Li Zhu, Meihua Song, Lien-Chieh Lee, Wei Zhou, Junjun Liu, Ting Wu and Xudong Yuan
ISPRS Int. J. Geo-Inf. 2026, 15(7), 322; https://doi.org/10.3390/ijgi15070322 - 16 Jul 2026
Viewed by 411
Abstract
Rapid urbanization and climate change have intensified environmental pressures and social inequalities, making the integration of urban resilience and sustainable development a critical global challenge, with Nature-based Solutions (NbS) emerging as a promising pathway; however, the knowledge structure, collaboration patterns, and evolutionary trends [...] Read more.
Rapid urbanization and climate change have intensified environmental pressures and social inequalities, making the integration of urban resilience and sustainable development a critical global challenge, with Nature-based Solutions (NbS) emerging as a promising pathway; however, the knowledge structure, collaboration patterns, and evolutionary trends of NbS research remain fragmented and insufficiently understood. This study conducts a comprehensive bibliometric analysis of 1261 publications from the Web of Science Core Collection (2005–2025), employing tools including VOSviewer 1.6.20, CiteSpace 6.4.R1, and Bibliometrix 4.1.3 to map publication trends, collaboration networks, knowledge bases, and thematic evolution. The results reveal a rapid expansion of NbS research since 2013, characterized by strong interdisciplinarity and a multicentric yet uneven geographical distribution dominated by China, the United States, and Europe. Four major research clusters are identified, encompassing policy governance, environmental benefits, ecosystem services, and social equity, reflecting a shift from ecological performance to integrated socio-ecological frameworks. Additionally, thematic evolution indicates growing emphasis on governance mechanisms, public health, and environmental justice. Overall, NbS research is transitioning toward a multi-scale, multi-objective, and governance-oriented paradigm. These findings highlight the need for strengthened international collaboration, standardized evaluation frameworks, and inclusive policy design to enhance the effectiveness and global applicability of NbS in advancing urban sustainable development. Full article
(This article belongs to the Special Issue Novel Theories and Applications on Geo-Spatial Databases, Models and AI in Urban Science, Planning, Development and Governance)
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