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Feature Papers for Land Innovations—Data and Machine Learning: 2nd...
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Feature Papers for Land Innovations—Data and Machine Learning: 2nd Edition

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5801

Special Issue Editor

Prof. Dr. Chuanrong Zhang

E-Mail Website
Guest Editor
Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Rd., Unit 4148, Storrs, CT 06269, USA
Interests: geographical information science and systems; cyberinfrastructure; land use and land cover; spatial data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue “Feature Papers for Land Innovations—Data and Machine Learning II” welcomes contributions relating to spatial data science for obtaining, processing, analyzing, harnessing, and visualizing social, economic, environmental, and other data related to land. Particularly, it welcomes geospatial artificial intelligence and machine learning techniques for dealing with spatial big data, including remotely sensed data and social media data. Manuscripts can be theoretical, applied, or review articles. Interdisciplinary manuscripts are particularly welcome.

Prof. Dr. Chuanrong Zhang
Guest Editor

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.

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Keywords

  • spatial big data 
  • land use and land cover
  • geospatial artificial intelligence
  • machine learning
  • deep learning
  • big data processing
  • big data analysis
  • big data visualization

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Related Special Issue

Published Papers (6 papers)

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Research

22 pages, 8643 KiB  
Article
Spatial Expansion Characteristics and Nonlinear Relationships of Driving Factors in Urban Agglomerations: A Case Study of the Yangtze River Delta Urban Agglomeration in China
by Bochuan Zhao, Yifei Wang, Huizhi Geng, Xuan Jiang and Lingyue Li
Land 2024, 13(11), 1951; https://doi.org/10.3390/land13111951 - 19 Nov 2024
Viewed by 247
Abstract
Urban agglomerations are increasingly becoming the primary regional units in global competition, characterized by the rapid expansion of impervious surface areas, which negatively impacts both society and the environment. This study quantifies the spatiotemporal expansion of these surfaces in the Yangtze River Delta [...] Read more.
Urban agglomerations are increasingly becoming the primary regional units in global competition, characterized by the rapid expansion of impervious surface areas, which negatively impacts both society and the environment. This study quantifies the spatiotemporal expansion of these surfaces in the Yangtze River Delta urban agglomeration and explores its driving factors using a Geographically Weighted Random Forest model. The results demonstrate a transition from “point expansion” to “infill development”, while also revealing a gradual southward shift in the developmental focus of the Yangtze River Delta urban agglomeration. Although expansion intensity has decreased, spatial clustering has intensified. Based on the expansion patterns of impervious surface areas, we propose a novel regional classification method, dividing the Yangtze River Delta urban agglomeration into three zones: “A-Development Decline Zone”, “B-Development Core Zone”, and “C-Development Ascendance Zone”. Socio-economic factors are the primary drivers of this expansion, followed by science and education, and then the ecological environment, while physical geography factors have the least impact. The study reveals differentiated regional development characteristics and further refines the sub-regions within the urban agglomeration, providing a new perspective for future regional coordinated development policies. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 2nd Edition)
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19 pages, 5871 KiB  
Article
High-Resolution Land Use Land Cover Dataset for Meteorological Modelling—Part 2: ECOCLIMAP-SG-ML an Ensemble Land Cover Map
by Thomas Rieutord, Geoffrey Bessardon and Emily Gleeson
Land 2024, 13(11), 1875; https://doi.org/10.3390/land13111875 - 9 Nov 2024
Viewed by 386
Abstract
While the surface of the Earth plays a key role in weather forecasting through its interaction with the atmosphere, in ensemble numerical weather predictions the uncertainty on the surface is only represented with perturbations in the parameterisations representing the surface processes. Data representing [...] Read more.
While the surface of the Earth plays a key role in weather forecasting through its interaction with the atmosphere, in ensemble numerical weather predictions the uncertainty on the surface is only represented with perturbations in the parameterisations representing the surface processes. Data representing the surface, such as the land cover, are not perturbed. As fully data-driven forecasts without parameterisations are growing in importance, sampling the uncertainty on the land cover data brings a new way of making ensemble forecasts. Our work describes a method of generating ensemble land cover maps for numerical weather prediction. The target land cover map has the ECOCLIMAP-SG labels used in the SURFEX surface model and therefore is expected to have all relevant labels for surface-atmosphere interactions. The method translates the ESA WorldCover map to ECOCLIMAP-SG labels and resolution using auto-encoders. The land cover ensemble members are obtained by sampling the land cover probabilities in the output of the neural network. This paper builds upon the work done in a companion paper describing the high-resolution version of ECOCLIMAP-SG, called ECOCLIMAP-SG+, used for the training and evaluation of the neural network. The output map presented here, called ECOCLIMAP-SG-ML, improves upon the ECOCLIMAP-SG map in terms of resolution (from 300 m to 60 m), overall accuracy (from 0.41 to 0.63), and the ability to produce ensemble members. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 2nd Edition)
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29 pages, 4932 KiB  
Article
High-Resolution Land Use Land Cover Dataset for Meteorological Modelling—Part 1: ECOCLIMAP-SG+ an Agreement-Based Dataset
by Geoffrey Bessardon, Thomas Rieutord, Emily Gleeson, Bolli Pálmason and Sandro Oswald
Land 2024, 13(11), 1811; https://doi.org/10.3390/land13111811 - 1 Nov 2024
Viewed by 590
Abstract
ECOCLIMAP-SG+ is a new 60 m land use land cover dataset, which covers a continental domain and represents the 33 labels of the original ECOCLIMAP-SG dataset. ECOCLIMAP-SG is used in HARMONIE-AROME, the numerical weather prediction model used operationally by Met Éireann and other [...] Read more.
ECOCLIMAP-SG+ is a new 60 m land use land cover dataset, which covers a continental domain and represents the 33 labels of the original ECOCLIMAP-SG dataset. ECOCLIMAP-SG is used in HARMONIE-AROME, the numerical weather prediction model used operationally by Met Éireann and other national meteorological services. ECOCLIMAP-SG+ was created using an agreement-based method to combine information from many maps to overcome variations in semantic and geographical coverage, resolutions, formats, accuracy, and representative periods. In addition to ECOCLIMAP-SG+, the process generates an agreement score map, which estimates the uncertainty of the land cover labels in ECOCLIMAP-SG+ at each location in the domain. This work presents the first evaluation of ECOCLIMAP-SG and ECOCLIMAP-SG+ against the following trusted land cover maps: LUCAS 2022, the Irish National Land Cover 2018 dataset, and an Icelandic version of ECOCLIMAP-SG. Using a set of primary labels, ECOCLIMAP-SG+ outperforms ECOCLIMAP-SG regarding the F1-score against LUCAS 2022 over Europe and the Irish national land cover 2018 dataset. Similarly, it outperforms ECOCLIMAP-SG against the Icelandic version of ECOCLIMAP-SG for most of the represented secondary labels. The score map shows that the quality ECOCLIMAP-SG+ is hetereogeneous. It could be improved once new maps become available, but we do not control when they will be available. Therefore, the second part of this publication series aims at improving the map using machine learning. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 2nd Edition)
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18 pages, 2999 KiB  
Article
Research on Site Selection Planning of Urban Parks Based on POI and Machine Learning—Taking Guangzhou City as an Example
by Xiaoxiang Tang, Cheng Zou, Chang Shu, Mengqing Zhang and Huicheng Feng
Land 2024, 13(9), 1362; https://doi.org/10.3390/land13091362 - 26 Aug 2024
Viewed by 737
Abstract
Against the background of smart city construction and the increasing application of big data in the field of planning, a method is proposed to effectively improve the objectivity, scientificity, and global nature of urban park siting, taking Guangzhou and its current urban park [...] Read more.
Against the background of smart city construction and the increasing application of big data in the field of planning, a method is proposed to effectively improve the objectivity, scientificity, and global nature of urban park siting, taking Guangzhou and its current urban park layout as an example. The proposed approach entails integrating POI data and innovatively applying machine learning algorithms to construct a decision tree model to make predictions for urban park siting. The results show that (1) the current layout of urban parks in Guangzhou is significantly imbalanced and has blind zones, and with an expansion of the search radius, the distribution becomes more concentrated; high-density areas decrease from the center outward in a circle, which manifests as a pattern of high aggregation at the core and low dispersion at the edge. (2) Urban park areas with a service pressure of level 3 have the largest coverage and should be prioritized for construction as much as possible; there are fewer areas at levels 4 and 5, which are mainly concentrated in the central city, and unreasonable resource allocation is a problem that needs to be solved urgently. (3) There was a preliminary prediction of 6825 sites suitable for planning, and the fit with existing city parks was 93.7%. The prediction results were reasonable, and the method was feasible. After further screening through the coupling and superposition of the service pressure and the layout status quo, 1537 locations for priority planning were finally obtained. (4) Using the ID3 machine learning algorithm to predict urban park sites is conducive to the development of an overall optimal layout, and subjectivity in site selection can be avoided, providing a methodological reference for the planning and construction of other infrastructure or the optimization of layouts. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 2nd Edition)
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26 pages, 23866 KiB  
Article
Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis
by Kerun Li
Land 2024, 13(8), 1161; https://doi.org/10.3390/land13081161 - 29 Jul 2024
Viewed by 1121
Abstract
Urban space constitutes a complex system, the quality of which directly impacts the quality of life for residents. In high-density cities, factors such as the green coverage in street spaces, color richness, and accessibility of services are crucial elements affecting daily life. Moreover, [...] Read more.
Urban space constitutes a complex system, the quality of which directly impacts the quality of life for residents. In high-density cities, factors such as the green coverage in street spaces, color richness, and accessibility of services are crucial elements affecting daily life. Moreover, the application of advanced technologies, such as deep learning combined with street view image analysis, has certain limitations, especially in the context of high-density urban streets. This study focuses on the street space quality within the urban fabric of the Macau Peninsula, exploring the characteristics of the street space quality within the context of high-density urban environments. By leveraging street view imagery and multi-source urban data, this research employs principal component analysis (PCA) and deep-learning techniques to conduct a comprehensive analysis and evaluation of the key indicators of street space quality. Utilizing semantic segmentation and ArcGIS technology, the study quantifies 16 street space quality indicators. The findings reveal significant variations in service-related indicators such as the DLS, ALS, DCE, and MFD, reflecting the uneven distribution of service facilities. The green coverage index and color richness index, along with other service-related indicators, are notably influenced by tourism and commercial activities. Correlation analysis indicates the presence of land-use conflicts between green spaces and service facilities in high-density urban settings. Principal component analysis uncovers the diversity and complexity of the indicators, with cluster analysis categorizing them into four distinct groups, representing different combinations of spatial quality characteristics. This study innovatively provides a quantitative assessment of street space quality, emphasizing the importance of considering multiple key factors to achieve coordinated urban development and enhance spatial quality. The results offer new perspectives and methodologies for the study of street space quality in high-density urban environments. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 2nd Edition)
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21 pages, 326 KiB  
Article
Effects of Big Data on PM2.5: A Study Based on Double Machine Learning
by Xinyu Wei, Mingwang Cheng, Kaifeng Duan and Xiangxing Kong
Land 2024, 13(3), 327; https://doi.org/10.3390/land13030327 - 4 Mar 2024
Cited by 1 | Viewed by 1574
Abstract
The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This [...] Read more.
The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This study employs a double machine learning model to explore the impact of big data development on the PM2.5 concentration in 277 prefecture-level cities across China. This analysis is grounded in the quasi-natural experiment named the National Big Data Comprehensive Pilot Zone. The findings reveal a significant inverse relationship between big data development and PM2.5 levels, with a correlation coefficient of −0.0149, a result consistently supported by various robustness checks. Further mechanism analyses elucidate that big data development markedly diminishes PM2.5 levels through the avenues of enhanced urban development and land use planning. The examination of heterogeneity underscores big data’s suppressive effect on PM2.5 levels across central, eastern, and western regions, as well as in both resource-dependent and non-resource-dependent cities, albeit with varying degrees of significance. This study offers policy recommendations for the formulation and execution of big data policies, emphasizing the importance of acknowledging local variances and the structural nuances of urban economies. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 2nd Edition)
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