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Land
Special Issues
GeoAI Application in Urban Land Use and Urban Climate
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GeoAI Application in Urban Land Use and Urban Climate

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: 10 July 2026 | Viewed by 2440

Special Issue Editors

Dr. Chunxiao Zhang

E-Mail Website
Guest Editor
Department of Geographic Information Science, School of Information Engineering, China University of Geosciences, Beijing, China
Interests: knowledge- and data-driven geographic simulation; virtual geographic environments; urban thermal environment simulation; urban flood control
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Hou

E-Mail Website
Guest Editor
Research Center of Natural Resources Survey and Monitoring, Chinese Academy of Surveying and Mapping, Beijing 100036, China
Interests: ecological modelling; simulation and optimization of ecological networks; quantification and assessment of landscape structure and landscape change; ecological indicators; environmental impact assessment; spatial statistics; remote sensing applications
Special Issues, Collections and Topics in MDPI journals
Dr. Dongya Liu

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Interests: cellular automata, system dynamics, agent-based model; urban flow, land use, urban expansion
Prof. Dr. Milan Konecny

E-Mail Website
Guest Editor
Laboratory on Geoinformatics and Cartography, Department of Geography, Faculty of Science, Masaryk University, Kotlarska 2, 61137 Brno, Czech Republic
Interests: disaster risk reduction; disaster mapping; context and adaptive cartography; health cartography; big spatial data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urbanization and climate change have significantly increased the challenges of managing urban land use and environmental sustainability. The rapid evolution of Geospatial Artificial Intelligence (GeoAI) has transformed urban research, offering cutting-edge approaches to analyzing land use dynamics, forecasting climate impacts, and enhancing sustainable development strategies. This Special Issue invites original research that explores the innovative applications of GeoAI in understanding, modeling, and managing urban environments. The aim is to advance urban land use planning, strengthen climate resilience, and promote sustainable urban growth.

By integrating advanced technologies such as remote sensing, spatiotemporal data fusion, and predictive modeling, GeoAI provides actionable insights into urban expansion, land degradation, and climate adaptation strategies. This Special Issue seeks to compile high-quality research (original studies and review papers) that deepen our understanding of GeoAI’s role in urban systems.

Topics of Interest

  • We welcome submissions on the following topics:
  • GeoAI for urban land use analysis;
  • GeoAI and urban climate studies;
  • AI-driven urban growth prediction;
  • Remote sensing and deep learning for urban environments;
  • Big data and machine learning for urban sustainability;
  • Spatial–temporal AI models for urban dynamics;
  • Urban heat island detection and mitigation using GeoAI;
  • Smart cities and AI-powered urban planning;
  • AI-based risk assessment for urban climate resilience;
  • Ethical considerations and governance in GeoAI applications.

We invite researchers to contribute original research articles and review papers that advance knowledge in these areas.

We look forward to your submissions and contributions to this important field.

Dr. Chunxiao Zhang
Dr. Wei Hou
Dr. Dongya Liu
Prof. Dr. Milan Konecny
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. Land 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 2600 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

  • GeoAI
  • urban land use
  • urban climate
  • remote sensing
  • machine learning
  • urban sustainability
  • smart cities
  • climate resilience

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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

20 pages, 6436 KB  
Article
Multi-Scenario Regional Spatial Simulation Based on the Unet++ Architecture: A Case Study of the Yangtze River Economic Belt
by Wei Wei, Zishun Zhang and Junnan Xia
Land 2026, 15(4), 657; https://doi.org/10.3390/land15040657 - 16 Apr 2026
Viewed by 300
Abstract
Exploring the evolutionary dynamics of urban, agricultural, and ecological spaces is critical for regional sustainable development and spatial governance. However, traditional spatial simulation methods based on Cellular Automata often struggle to accommodate top-down spatial regulation, non-linear development patterns, and coordinated regional growth. The [...] Read more.
Exploring the evolutionary dynamics of urban, agricultural, and ecological spaces is critical for regional sustainable development and spatial governance. However, traditional spatial simulation methods based on Cellular Automata often struggle to accommodate top-down spatial regulation, non-linear development patterns, and coordinated regional growth. The objective of this scientific research is to address these limitations by proposing a deep learning-based framework for simulating the future distribution of these three spaces. Utilizing the Unet++ model and integrating empirical data sources including multi-period remote sensing land-use mapping and prefecture-level socioeconomic statistical data, the framework predicts regional spatial patterns for the year 2030. Empirical results from the Yangtze River Economic Belt demonstrate that the model achieves high precision in large-scale spatial forecasting (with an average test accuracy of 99.32%) and effectively captures non-linear evolutionary characteristics. Predictions across various growth scenarios reveal that a moderate socioeconomic growth rate facilitates ecological preservation; controlling the expansion of urban space to approximately 20% by 2030 can prevent excessive resource depletion and regional imbalances. Consequently, it is recommended to implement the construction land increment targets outlined in current spatial planning to achieve a balance between economic growth and ecological protection. Full article
(This article belongs to the Special Issue GeoAI Application in Urban Land Use and Urban Climate)
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22 pages, 4649 KB  
Article
Regulating Effects of Blue–Green Spaces on Land Surface Temperature Based on Local Climate Zones: A Case Study of Suzhou (2000–2022)
by Yudan Liu, Chunxiao Zhang, Yazhou Qi and Hanguang Yu
Land 2026, 15(4), 618; https://doi.org/10.3390/land15040618 - 9 Apr 2026
Viewed by 428
Abstract
Rapid urbanization has intensified urban surface thermal stress, yet how blue–green spaces (BGs) are associated with land surface temperature (LST) under different urban morphological contexts remains insufficiently understood. Using Suzhou, China, as a case study, this study integrates Landsat imagery from five representative [...] Read more.
Rapid urbanization has intensified urban surface thermal stress, yet how blue–green spaces (BGs) are associated with land surface temperature (LST) under different urban morphological contexts remains insufficiently understood. Using Suzhou, China, as a case study, this study integrates Landsat imagery from five representative years (2000, 2005, 2010, 2016, and 2022) with a 100 m local climate zone (LCZ) dataset to examine BGs–LST relationships over time. Two BGs indicators are considered: BGs proportion and the within-grid local dispersion of BGs, represented by BGs_std. The results show that LST in Suzhou’s built-up area exhibits a “rise–decline–rise” pattern during the study period, whereas BGs proportions evolve differently across LCZ types. Regression slope analysis shows that higher BGs proportion is generally associated with lower LST across most LCZ types and study years. Relatively stable negative associations are observed in LCZ 2, LCZ 3, LCZ 6, LCZ 9, and LCZ 10. Pearson correlation analysis further shows that BGs_std is generally positively associated with LST and that this relationship tends to strengthen over time. Relatively stronger associations are observed in LCZ 1, LCZ 3, LCZ 5, and LCZ 6 in some years. These findings suggest that BGs–LST relationships should be interpreted not only in terms of BGs proportion, but also in relation to urban form and within-unit BGs organization. This study provides an LCZ-based empirical perspective on BGs–LST associations in the context of a rapidly urbanizing city. Full article
(This article belongs to the Special Issue GeoAI Application in Urban Land Use and Urban Climate)
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19 pages, 4115 KB  
Article
Urban Remote Sensing Ecological Quality Assessment Based on Hierarchical Principal Component Analysis and Water Factor Enhancement: A Case Study of Linyi City, Shandong Province, China
by Xiaocai Liu, Xianglong Liu, Xinqi Zheng, Xiaoyang Liu, Guangting Yu, Fei Jiang and Kun Liu
Land 2026, 15(1), 196; https://doi.org/10.3390/land15010196 - 21 Jan 2026
Cited by 1 | Viewed by 584
Abstract
Rapid urbanization has significantly affected urban ecological environments, necessitating accurate and scientific quality assessments. In this study, we develop an enhanced remote sensing ecological index (WRSEI) for water network cities using Linyi City, China, as a case study. Key innovations include (1) introducing [...] Read more.
Rapid urbanization has significantly affected urban ecological environments, necessitating accurate and scientific quality assessments. In this study, we develop an enhanced remote sensing ecological index (WRSEI) for water network cities using Linyi City, China, as a case study. Key innovations include (1) introducing a water–vegetation index to better represent aquatic ecosystems; (2) incorporating nighttime light data to quantify the intensity of human activity; and (3) employing hierarchical PCA to rationally weight ecological endowment and stress indicators. The model’s effectiveness was rigorously validated using independent land use data. The results show that (1) the WRSEI accurately captures Linyi’s “water–city symbiosis” pattern, increasing the assessed ecological quality of water bodies by 15.78% compared to the conventional RSEI; (2) hierarchical PCA provides more ecologically reasonable indicator weights; and (3) from 2000 to 2020, ecological quality exhibited a pattern of “central degradation and peripheral improvement”, driven by urban expansion. This study establishes a validated technical framework for ecological assessment in water-rich cities, offering a scientific basis for sustainable urban management. Full article
(This article belongs to the Special Issue GeoAI Application in Urban Land Use and Urban Climate)
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