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Land
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GeoAI for Urban Sustainability Monitoring and Analysis
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GeoAI for Urban Sustainability Monitoring and Analysis

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5951

Special Issue Editors

Dr. Nan Xu

E-Mail Website
Guest Editor
1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong
2. College of Geography and Remote Sensing, Hohai University, Nanjing 210098, China
Interests: coastal remote sensing; water resource remote sensing; GeoAI
Special Issues, Collections and Topics in MDPI journals
Dr. Yifu Ou

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong
Interests: urban sustainability; environmental economics; air quality remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jixiang Liu

E-Mail Website
Guest Editor
Department of Urban Planning, School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
Interests: transport and land use; travel behavior; urban mobility; urban vibrancy; machine learning; spatial analysis; big data analytics; health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

GeoAI, or geographic artificial intelligence, is a powerful tool used for urban sustainability monitoring, analysis, and prediction by combining innovative artificial intelligence methods from space science, machine learning, deep learning, data mining, and cloud computing from big earth data. GeoAI plays a key role in pushing geographic information science (GIS) and earth observation toward a new stage of development by enhancing traditional geospatial analysis and mapping. By combining remote sensing data and GeoAI, we can classify and map land cover, track temporal changes in land use, and predict future trends regarding urban sustainability for better planning, management, and decision making. In summary, GeoAI exhibits vast potential to contribute to urban sustainability in the future.

In this Special Issue, we seek the submission of groundbreaking research and case studies that demonstrate urban-sustainability-related applications and advances in geographic artificial intelligence. Relevant topics include, but are not limited to, the following:

Geospatial artificial intelligence (geospatial AI or GeoAI) for urban land use and land cover mapping;
GeoAI for urban monitoring, modeling, analysis, and prediction;
AI in geostatistics and spatiotemporal urban-related modeling and simulation;
AI For urban-related geospatial data acquisition, precessing, and analysis;
AI For sustainability monitoring and evaluation in urban areas;
Big data and machine learning for urban studies.

The goal of this Special Issue is to collect papers (original research articles and review papers) that provide insights into GeoAI for urban sustainability monitoring and analysis. This Special Issue welcomes manuscripts that link the following themes:

  • Urban;
  • Sustainability;
  • GeoAI;
  • Remote sensing;
  • Big data;
  • Geography.

We look forward to receiving your original research articles and reviews.

Dr. Nan Xu
Dr. Yifu Ou
Dr. Jixiang Liu
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

  • urban
  • sustainability
  • GeoAI
  • remote sensing
  • big data
  • geography

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

Published Papers (4 papers)

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Research

16 pages, 5740 KiB  
Article
Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach
by Zhenbao Wang, Shuyue Liu, Haitao Lian and Xinyi Chen
Land 2024, 13(8), 1302; https://doi.org/10.3390/land13081302 - 16 Aug 2024
Cited by 2 | Viewed by 806
Abstract
Understanding the relationship between the demand for public transportation and land use is critical to promoting public-transportation-oriented urban development. Taking Beijing as an example, we took the Public Transportation Index (PTI) during the working day’s early peak hours as the dependent variable. And [...] Read more.
Understanding the relationship between the demand for public transportation and land use is critical to promoting public-transportation-oriented urban development. Taking Beijing as an example, we took the Public Transportation Index (PTI) during the working day’s early peak hours as the dependent variable. And 15 land use and built environment variables were selected as the independent variables according to the “7D” built environment dimensions. According to the Modifiable Areal Unit Problem (MAUP), the size and shape of the spatial units will affect the aggregation results of the dependent variable and the independent variables. To find the ideal spatial unit division method, we assess how well the nonlinear model fits several spatial units. Extreme Gradient Boosting (XGBoost) was utilized to investigate the nonlinear effects of the built environment on PTI and threshold effects based on the ideal spatial unit. The results show that (1) the best spatial unit division method is based on traffic analysis zones (TAZs); (2) the top four explanatory variables affecting PTI are, in order: mean travel distance, residential density, subway station density, and public services density; (3) there are nonlinear relationships and significant threshold effects between the land use variables and PTI. The priority regeneration TAZs were identified according to the intersection analysis of the low PTI TAZs set and the PTI-sensitive TAZs set based on different land use variables. Prioritized urban regeneration TAZs require targeted strategies, and the results of the study may provide a scientific basis for proposing strategies to renew land use to increase PTI. Full article
(This article belongs to the Special Issue GeoAI for Urban Sustainability Monitoring and Analysis)
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19 pages, 5156 KiB  
Article
A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning
by Nicolás Valenzuela-Levi, Nicolás Gálvez Ramírez, Cristóbal Nilo, Javiera Ponce-Méndez, Werner Kristjanpoller, Marcos Zúñiga and Nicolás Torres
Land 2024, 13(8), 1211; https://doi.org/10.3390/land13081211 - 6 Aug 2024
Cited by 1 | Viewed by 1544
Abstract
Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a ‘cyborg walk’ method and apply it to studying litter in public spaces. Walking routes are [...] Read more.
Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a ‘cyborg walk’ method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this ‘cyborg walk’ experiment and the seminal cyborg metaphor proposed by Donna Haraway. Full article
(This article belongs to the Special Issue GeoAI for Urban Sustainability Monitoring and Analysis)
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24 pages, 10210 KiB  
Article
Integration between Dockless Bike-Sharing and Buses: The Effect of Urban Road Network Characteristics
by Zhaowei Yin, Yuanyuan Guo, Mengshu Zhou, Yixuan Wang and Fengliang Tang
Land 2024, 13(8), 1209; https://doi.org/10.3390/land13081209 - 5 Aug 2024
Cited by 1 | Viewed by 1423
Abstract
Globally, dockless bike-sharing (DBS) systems are acclaimed for their convenience and seamless integration with public transportation, such as buses and metros. While much research has focused on the connection between the built environment and the metro–DBS integration, the influence of urban road characteristics [...] Read more.
Globally, dockless bike-sharing (DBS) systems are acclaimed for their convenience and seamless integration with public transportation, such as buses and metros. While much research has focused on the connection between the built environment and the metro–DBS integration, the influence of urban road characteristics on DBS and bus integration remains underexplored. This study defined the parking area of DBS around bus stops by a rectangular buffer so as to extract the DBS–bus integration, followed by measuring the access and egress integration using real-time data on dockless bike locations. This indicated that the average trip distance for DBS–bus access and egress integration corresponded to 1028.47 m and 1052.33 m, respectively. A zero-inflated negative binomial (ZINB) regression model assessed how urban roads and other transportation facilities correlate with DBS–bus integration across various scenarios. The findings revealed that certain street patterns strongly correlate with frequent connection hotspots. Furthermore, high-grade roads and ‘dense loops on a stick’ street types may negatively influence DBS–bus integration. The increase in the proportion of three-legged intersections and culs-de-sac in the catchment makes it difficult for bus passengers to transfer by DBS. These insights offer valuable guidance for enhancing feeder services in public transit systems. Full article
(This article belongs to the Special Issue GeoAI for Urban Sustainability Monitoring and Analysis)
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18 pages, 16041 KiB  
Article
Dynamic Inversion Method of Calculating Large-Scale Urban Building Height Based on Cooperative Satellite Laser Altimetry and Multi-Source Optical Remote Sensing
by Haobin Xia, Jianjun Wu, Jiaqi Yao, Nan Xu, Xiaoming Gao, Yubin Liang, Jianhua Yang, Jianhang Zhang, Liang Gao, Weiqi Jin and Bowen Ni
Land 2024, 13(8), 1120; https://doi.org/10.3390/land13081120 - 24 Jul 2024
Viewed by 1185
Abstract
Building height is a crucial indicator when studying urban environments and human activities, necessitating accurate, large-scale, and fine-resolution calculations. However, mainstream machine learning-based methods for inferring building heights face numerous challenges, including limited sample data and slow update frequencies. Alternatively, satellite laser altimetry [...] Read more.
Building height is a crucial indicator when studying urban environments and human activities, necessitating accurate, large-scale, and fine-resolution calculations. However, mainstream machine learning-based methods for inferring building heights face numerous challenges, including limited sample data and slow update frequencies. Alternatively, satellite laser altimetry technology offers a reliable means of calculating building heights with high precision. Here, we initially calculated building heights along satellite orbits based on building-rooftop contour vector datasets and ICESat-2 ATL03 photon data from 2019 to 2022. By integrating multi-source passive remote sensing observation data, we used the inferred building height results as reference data to train a random forest model, regressing building heights at a 10 m scale. Compared with ground-measured heights, building height samples constructed from ICESat-2 photon data outperformed methods that indirectly infer building heights using total building floor number. Moreover, the simulated building heights strongly correlated with actual observations at a single-city scale. Finally, using several years of inferred results, we analyzed building height changes in Tianjin from 2019 to 2022. Combined with the random forest model, the proposed model enables large-scale, high-precision inference of building heights with frequent updates, which has significant implications for global dynamic observation of urban three-dimensional features. Full article
(This article belongs to the Special Issue GeoAI for Urban Sustainability Monitoring and Analysis)
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