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Land
Special Issues
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: 31 December 2024 | Viewed by 683

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

Published Papers (1 paper)

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Research

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 256
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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