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Land
Special Issues
Application of Multi-Source Geographical Big Data in Land Use Decision-Making
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Application of Multi-Source Geographical Big Data in Land Use Decision-Making

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2020

Special Issue Editors

Dr. Shi Shen

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 10010, China
Interests: GIS; sustainability; LUCC; land use simulation; GeoAI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Miguel Martínez-Graña

E-Mail Website
Guest Editor
Department of Geology, Faculty of Sciences, Merced Square, University of Salamanca, 37008 Salamanca, Spain
Interests: applied geomorphology; remote sensing and GIS; environmental geology; geomorphology and geological risk
Special Issues, Collections and Topics in MDPI journals
Dr. Sijing Ye

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: arable land monitoring; evaluation and protection; geographic spatiotemporal data analysis; land use change simulation and evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Zhuolin Tao

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: urban geography and urban science; urban spatial structures; public service layout; geographic big data analysis
Dr. Min Zhao

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: urban remote sensing; nighttime light remote sensing

Special Issue Information

Dear Colleagues,

Land use decision-making is a crucial component of urban planning and development worldwide, necessitating the consideration of numerous factors influencing these decisions. The emergence of big data has unlocked a wealth of geospatial information that can be harnessed to facilitate informed and sustainable choices in land use planning across various regions. This Special Issue delves into the application of multi-source geographical big data in land use decision-making, emphasizing innovative methods, case studies, and best practices that can optimize land use allocation and foster sustainable development on a global scale.

This Special Issue will cover a range of topics, including:

  1. Data acquisition and preprocessing techniques for geospatial big data in diverse geographical contexts;
  2. Machine learning algorithms and deep learning architectures for analyzing multi-source geospatial data across various regions;
  3. The integration of remote sensing data, social media data, and other geospatial data sources for land use classification in different countries;
  4. Urban function mapping using geospatial big data in international urban centers;
  5. Decision support systems for land use planning and management in both developed and developing nations.

By examining these topics, this Special Issue aspires to advance land use decision-making processes on a global scale, promote sustainable urban development across various contexts, and encourage the effective utilization of geospatial big data in urban planning worldwide. We invite a wide range of scientific contributions to be considered for publication in this Special Issue. This includes but is not limited to, empirical studies, research articles, critical reviews, case studies, comparative analyses, and methodological innovations that advance our understanding of the application of multi-source geographical big data in land use decision-making.

Dr. Shi Shen
Prof. Dr. Antonio Miguel Martínez-Graña
Dr. Sijing Ye
Dr. Zhuolin Tao
Dr. Min Zhao
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. 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

  • geographic information systems (GISs)
  • land use decision-making
  • land use simulation
  • GeoAI
  • multi-source data integration
  • geospatial big data
  • land use decision-making
  • remote sensing data
  • land use classification
  • land management
  • global land use patterns

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

Published Papers (4 papers)

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Research

19 pages, 3801 KiB  
Article
AI-Based Identification and Redevelopment Prioritization of Inefficient Industrial Land Using Street View Imagery and Multi-Criteria Modeling
by Yan Yu, Qiqi Yan, Yu Guo, Chenhe Zhang, Zhixiang Huang and Liangze Lin
Land 2025, 14(6), 1254; https://doi.org/10.3390/land14061254 - 11 Jun 2025
Abstract
The strategic prioritization of inefficient industrial land (IIL) redevelopment is critical for directing capital allocation toward sustainable urban regeneration. However, current redevelopment prioritization suffers from inefficient identification of IIL and ambiguous characterization of redevelopment potential, which hinders the efficiency of land resource allocation. [...] Read more.
The strategic prioritization of inefficient industrial land (IIL) redevelopment is critical for directing capital allocation toward sustainable urban regeneration. However, current redevelopment prioritization suffers from inefficient identification of IIL and ambiguous characterization of redevelopment potential, which hinders the efficiency of land resource allocation. To address these challenges, this study develops an AI-driven redevelopment prioritization framework for identifying IIL, evaluating redevelopment potential, and establishing implementation priorities. For land identification we propose an improved YOLOv11 model with an AdditiveBlock module to enhance feature extraction in complex street view scenes, achieving an 80.1% mAP on a self-built dataset of abandoned industrial buildings. On this basis, a redevelopment potential evaluation index system is constructed based on the necessity, maturity, and urgency of redevelopment, and the Particle Swarm Optimization-Projection Pursuit (PSO-PP) model is introduced to objectively evaluate redevelopment potential by adaptively reducing the reliance on expert judgment. Subsequently, the redevelopment priorities were classified according to the calculated potential values. The proposed framework is empirically tested in the central urban area of Ningbo City, China, where inefficient industrial land is successfully identified and redevelopment priority is categorized into near-term, medium-term, and long-term stages. Results show that the framework integrating computer vision and machine learning technology can effectively provide decision support for the redevelopment of IIL and offer a new method for promoting the smart growth of urban space. Full article
(This article belongs to the Special Issue Application of Multi-Source Geographical Big Data in Land Use Decision-Making)
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27 pages, 16217 KiB  
Article
Source Apportionment and Ecological-Health Risk Assessments of Potentially Toxic Elements in Topsoil of an Agricultural Region in Southwest China
by Yangshuang Wang, Shiming Yang, Denghui Wei, Haidong Li, Ming Luo, Xiaoyan Zhao, Yunhui Zhang and Ying Wang
Land 2025, 14(6), 1192; https://doi.org/10.3390/land14061192 - 2 Jun 2025
Viewed by 281
Abstract
Soil potentially toxic element (PTE) contamination remains a global concern, particularly in rural agricultural regions. This study collected 157 agricultural topsoil samples within a rural area in SW China. Combined with multivariate statistical analysis in the compositional data analysis (CoDa) perspective, the PMF [...] Read more.
Soil potentially toxic element (PTE) contamination remains a global concern, particularly in rural agricultural regions. This study collected 157 agricultural topsoil samples within a rural area in SW China. Combined with multivariate statistical analysis in the compositional data analysis (CoDa) perspective, the PMF model was applied to identify key contamination sources and quantify their contributions. Potential ecological risk assessment and Monte Carlo simulation were employed to estimate ecological-health risks associated with PTE exposure. The results revealed that the main exceeding PTEs (Mercury—Hg and Cadmium—Cd) are rich in urbanized areas and the GFGP (Grain for Green Program) regions. Source apportionment indicated that soil parent materials constituted the dominant contributor (32.48%), followed by traffic emissions (28.31%), atmospheric deposition (21.48%), and legacy agricultural effects (17.86%). Ecological risk assessment showed that 60.51% of soil samples exhibited higher potential ecological risk (PERI > 150), with moderate-risk areas concentrated in the GFGP regions. The elements Cd and Hg from legacy agricultural effects and atmospheric deposition contributed the most to ecological risk. Health risk assessment demonstrated that most risk indices fell within acceptable ranges for all populations, while only children showed elevated non-carcinogenic risk (THImax > 1.0). Among PTEs, the element As, mainly from traffic emissions, was identified as a priority control element due to its significant health implications. Geospatial distributions showed significant risk enrichment in the GFGP regions (legacy agricultural areas). These findings present associated risk levels in sustainable agricultural regions, providing valuable data to support soil environmental management in regions requiring urgent intervention worldwide. Full article
(This article belongs to the Special Issue Application of Multi-Source Geographical Big Data in Land Use Decision-Making)
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22 pages, 11311 KiB  
Article
Quality Analysis for Conservation and Integral Risk Assessment of the Arribes del Duero Natural Park (Spain)
by Leticia Merchán, Antonio Miguel Martínez-Graña and Carlos E. Nieto
Land 2025, 14(4), 885; https://doi.org/10.3390/land14040885 - 17 Apr 2025
Viewed by 362
Abstract
The environment is being affected by the great development of human activities, which is why, in recent years, the need to protect the environment has increased, through the carrying out of a Strategic Environmental Assessment (SEA). Within this assessment, environmental geology constitutes an [...] Read more.
The environment is being affected by the great development of human activities, which is why, in recent years, the need to protect the environment has increased, through the carrying out of a Strategic Environmental Assessment (SEA). Within this assessment, environmental geology constitutes an instrument for territorial and urban planning based on the analysis of conservation and the integral analysis of risks, obtaining cartography that can be useful in territorial and regional planning strategies. The methodology carried out in this article consists of applying a multi-criteria analysis in territorial planning, combining vector and raster data. This novel, low-cost, and effective methodology assesses conservation areas and risks, using map algebra and network analysis to identify priority areas and facilitate decision-making in a precise and quantitative manner. This analysis has been carried out in the Arribes del Duero Natural Park, which stands out as a place where numerous environmental values coexist, i.e., geological, geomorphological, and edaphological, forming unique landscapes. With regard to the results obtained, the cartography of conservation quality classifies the territory into four categories according to its degree of conservation: very high, high, low, and very low quality. The integral risk cartography identifies the areas with the greatest geological risks, such as erosion and landslides, and establishes limitations for land use. Also, by integrating both cartographies, it is determined which activities are compatible with each zone, considering both conservation and risks. Finally, it can be concluded that the cartographies obtained are useful for efficient land management, protecting the environment, and allowing human development in a controlled manner. Full article
(This article belongs to the Special Issue Application of Multi-Source Geographical Big Data in Land Use Decision-Making)
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17 pages, 3431 KiB  
Article
Interchangeability of Cross-Platform Orthophotographic and LiDAR Data in DeepLabV3+-Based Land Cover Classification Method
by Shijun Pan, Keisuke Yoshida, Satoshi Nishiyama, Takashi Kojima and Yutaro Hashimoto
Land 2025, 14(2), 217; https://doi.org/10.3390/land14020217 - 21 Jan 2025
Viewed by 728
Abstract
Riverine environmental information includes important data to collect, and the data collection still requires personnel’s field surveys. These on-site tasks still face significant limitations (i.e., hard or danger to entry). In recent years, as one of the efficient approaches for data collection, air-vehicle-based [...] Read more.
Riverine environmental information includes important data to collect, and the data collection still requires personnel’s field surveys. These on-site tasks still face significant limitations (i.e., hard or danger to entry). In recent years, as one of the efficient approaches for data collection, air-vehicle-based Light Detection and Ranging technologies have already been applied in global environmental research, i.e., land cover classification (LCC) or environmental monitoring. For this study, the authors specifically focused on seven types of LCC (i.e., bamboo, tree, grass, bare ground, water, road, and clutter) that can be parameterized for flood simulation. A validated airborne LiDAR bathymetry system (ALB) and a UAV-borne green LiDAR System (GLS) were applied in this study for cross-platform analysis of LCC. Furthermore, LiDAR data were visualized using high-contrast color scales to improve the accuracy of land cover classification methods through image fusion techniques. If high-resolution aerial imagery is available, then it must be downscaled to match the resolution of low-resolution point clouds. Cross-platform data interchangeability was assessed by comparing the interchangeability, which measures the absolute difference in overall accuracy (OA) or macro-F1 by comparing the cross-platform interchangeability. It is noteworthy that relying solely on aerial photographs is inadequate for achieving precise labeling, particularly under limited sunlight conditions that can lead to misclassification. In such cases, LiDAR plays a crucial role in facilitating target recognition. All the approaches (i.e., low-resolution digital imagery, LiDAR-derived imagery and image fusion) present results of over 0.65 OA and of around 0.6 macro-F1. The authors found that the vegetation (bamboo, tree, grass) and road species have comparatively better performance compared with clutter and bare ground species. Given the stated conditions, differences in the species derived from different years (ALB from year 2017 and GLS from year 2020) are the main reason. Because the identification of clutter species includes all the items except for the relative species in this research, RGB-based features of the clutter species cannot be substituted easily because of the 3-year gap compared with other species. Derived from on-site reconstruction, the bare ground species also has a further color change between ALB and GLS that leads to decreased interchangeability. In the case of individual species, without considering seasons and platforms, image fusion can classify bamboo and trees with higher F1 scores compared to low-resolution digital imagery and LiDAR-derived imagery, which has especially proved the cross-platform interchangeability in the high vegetation types. In recent years, high-resolution photography (UAV), high-precision LiDAR measurement (ALB, GLS), and satellite imagery have been used. LiDAR measurement equipment is expensive, and measurement opportunities are limited. Based on this, it would be desirable if ALB and GLS could be continuously classified by Artificial Intelligence, and in this study, the authors investigated such data interchangeability. A unique and crucial aspect of this study is exploring the interchangeability of land cover classification models across different LiDAR platforms. Full article
(This article belongs to the Special Issue Application of Multi-Source Geographical Big Data in Land Use Decision-Making)
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