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Applied Sciences
Special Issues
Geospatial Insights: Unleashing the Power of Big Data and GeoAI
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Geospatial Insights: Unleashing the Power of Big Data and GeoAI

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 1 July 2024 | Viewed by 1345

Special Issue Editors

Dr. Xuke Hu

E-Mail Website
Guest Editor
Data Science Institute, German Aerospace Center (DLR), 07745 Jena, Germany
Interests: GeoAI; VGI; geoparsing; indoor mapping and localization
Special Issues, Collections and Topics in MDPI journals
Dr. Yeran Sun

E-Mail Website
Guest Editor
Department of Geography, University of Lincoln, Lincoln LN6 7TS, UK
Interests: big data; GeoAI; spatial modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Shaohua Wang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: land use monitoring; big geospatial analytics; geospatial AI; spatial optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's rapidly changing world of geospatial sciences, combining geospatial big data and geographic artificial intelligence (GeoAI) is reshaping how we conduct scientific research and transforming practical applications in various fields. Geospatial big data, gathered from satellites, sensors, social media, citizen input, and diverse sources, provide an enormous amount of spatial information. At the same time, GeoAI, which combines artificial intelligence with geospatial analysis, offers innovative methods for understanding this vast data landscape. An essential component of GeoAI is the use of large language models (LLMs), enhancing natural language understanding within the geospatial domain. These models facilitate smooth communication between complex data patterns and human understanding.

This Special Issue delves into innovative approaches in geospatial big data and GeoAI, emphasizing data integration and advanced artificial intelligence techniques like large language models. Similar to the focus on natural products, our discussions center on modern geospatial methods and technologies, validated through practical applications in real-world scenarios.

This research topic welcomes original research papers and review papers offering new insights into geospatial big data and GeoAI. Topics of interest include, but are not limited to, the following:

  • Harvesting geospatial information from diverse data sources.
  • Harnessing AI for geospatial solutions across diverse fields.
  • Multi-source data fusion for enhanced geospatial analysis.
  • Enhancing disaster response through satellite imagery and social media data integration.
  • Natural language processing in geospatial data interpretation.
  • Semantic understanding in geospatial analysis using LLMs.
  • Enhanced spatial query systems with large language models.
  • GeoAI-driven sentiment analysis from social media texts.
  • LLMs in geospatial knowledge graph construction.
  • Interactive geospatial visualization with language-driven interfaces.
  • Geospatial question-answering systems using large language models.

Dr. Xuke Hu
Dr. Yeran Sun
Dr. Shaohua Wang
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • geospatial big data
  • GeoAI
  • large language models
  • geospatial analysis
  • social media data
  • remote sensing
  • VGI

Published Papers (1 paper)

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Research

23 pages, 7000 KiB  
Article
The Spatial Pattern and Influencing Factors of China’s Nighttime Economy Utilizing POI and Remote Sensing Data
by Guodong Yan, Lin Zou and Yunan Liu
Appl. Sci. 2024, 14(1), 400; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010400 - 01 Jan 2024
Viewed by 1062
Abstract
The nighttime economy (NTE) is one of the primary measures used by the Chinese government to promote urban consumption and capital flow. Especially after COVID-19, more regulations were introduced by both the central and local governments to accelerate this commercial activity. However, the [...] Read more.
The nighttime economy (NTE) is one of the primary measures used by the Chinese government to promote urban consumption and capital flow. Especially after COVID-19, more regulations were introduced by both the central and local governments to accelerate this commercial activity. However, the relationship between the NTE and urban development is controversial. There has been controversy over the relationship between the nighttime economy and urban development. We believe that organizations/individuals embedded in different regional contexts have different behavioral patterns, which, in turn, can make cities develop nighttime commercial activities differently. We wonder whether the nighttime economy’s large-scale development fits the diverse regional development. There is a lack of discussions of the spatial distribution of nighttime commercial activities from an urban geographical perspective, especially the differences and mechanisms of urban systems based on the nighttime economy. Based on existing research arguments, this article collects points of interest (POI) and nighttime light (NTL) remote sensing data (RSD) to spatialize nighttime economic activities in order to provide a reference for reasonable regional and urban economic planning. The nighttime economy (NTE) is one of the primary channels used by the Chinese government to promote urban consumption and capital flow, and the relationship between the NTE and urban development is controversial. Based on existing research, we selected the Yangtze River Delta (YRD) region as an example. We found that there are core–peripheral spatial patterns in nighttime commercial urban systems. The core is Shanghai, and provincial-level core cities form the second category, largely overlapping with the administrative urban system. Although the NTE is primarily concentrated in economically developed coastal areas, it spreads in the northwest–southeast direction, indicating that opportunities will arise in the geo-periphery. Although regulations encourage the growth of the NTE, infrastructure cannot fully support large-scale centralized expansion. The interaction of critical factors, such as urban policies, residents’ consumption, industrial structure, and economic foundations, may affect nighttime activities. Full article
(This article belongs to the Special Issue Geospatial Insights: Unleashing the Power of Big Data and GeoAI)
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