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AI-Based Spatial Planning and Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1807

Special Issue Editors


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Guest Editor
Department of Systems Research, Wroclaw University of Environmental and Life Sciences, Wrocław, Poland
Interests: geospatial analysis; geoAI; artificial intelligence; natural language processing; ontologies; semantic web technologies; urban planning; data analysis; space management; urban data science

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Guest Editor
Department of Agricultural Sciences, University of Naples, Federico II, 80055 Naples, Italy
Interests: agriculture; environment; soil analysis; fertilizer sustainability; sustainable agriculture; manure management; geographic information system; land-use change; historical GIS
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Special Issue Information

Dear Colleagues,

The implementation of artificial intelligence technological advances in urban environments provides significant prospects for innovation and effectiveness in spatial planning and management, ultimately contributing to a sustainable urban future. With the increasing complexity of cities, there is a growing demand for sophisticated tools to evaluate, anticipate, and optimize urban systems. Artificial intelligence, with its capacity to effectively handle and comprehend vast quantities of data and acquire knowledge, is becoming a transformative power in urban planning and analysis.

Urban planners may now efficiently evaluate large quantities of geospatial and temporal data by utilizing advanced technologies like machine learning, computer vision, and natural language processing. This empowers them to address complex urban issues in novel ways, including climate change adaptation, social justice, and resource optimization, with enhanced accuracy and foresight.

“AI-Based Spatial Planning and Analysis” welcomes submissions on this promising application area for artificial intelligence. This Special Issue explores the intersection of artificial intelligence and spatial planning, contributing to the ongoing discussion on smart cities, sustainable urban development, and the impact of technology on our living environment.

The call is open to a broad thematic range of papers covering the recent advanced approaches and applications of artificial intelligence in spatial planning and analysis, including but not limited to the following topics:

  • Urban development and regeneration strategies powered by AI;
  • Using artificial intelligence to analyze urban data and manage large data sets in spatial contexts;
  • AI-driven land use optimization and zoning;
  • Computer vision methods for enhancing spatial planning;
  • Natural language processing to analyze and incorporate textual data in the field of spatial planning;
  • Internet of Things (IoT) and sensor networks to collect and analyze spatial data in real time;
  • Geographic information systems (GIS) based on artificial intelligence for advanced spatial modeling in spatial planning;
  • Smart city initiatives and their implementation through AI technologies;
  • AI applications in transportation planning and mobility management;
  • The role of AI in urban resilience and climate change adaptation;
  • Integration of AI with other technologies (e.g., blockchain, augmented reality) for urban planning;
  • Case studies of successful AI implementation in spatial planning projects;
  • Challenges and limitations of AI adoption in urban planning contexts;
  • Future trends and potential paradigm shifts in AI-enabled spatial planning;
  • Ethical considerations and social implications of artificial intelligence (AI) in spatial planning.

We strongly encourage submissions comprising original research focusing on advancements in technology. However, we also welcome review papers and comparative studies.

Dr. Iwona Kaczmarek
Prof. Dr. Stefania Pindozzi
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 250 words) can be sent to the Editorial Office for assessment.

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

  • artificial intelligence
  • geospatial data analysis
  • spatial planning
  • urban development
  • AI-driven decision

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Published Papers (2 papers)

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Research

20 pages, 2807 KB  
Article
Exploring the Visibility Gap Between Public Investment and Media Discourse in the Wrocław Participatory Budget
by Patryk Mierzejewski, Klaudiusz Tomczyk, Grzegorz Chrobak and Iwona Kaczmarek
Appl. Sci. 2026, 16(5), 2265; https://doi.org/10.3390/app16052265 - 26 Feb 2026
Viewed by 304
Abstract
The purpose of this paper is to analyze the media visibility of investments implemented in Wrocław, with a particular focus on the democratization of urban processes through the Wrocław Participatory Budget (WPB) and to study the public perception of these projects within the [...] Read more.
The purpose of this paper is to analyze the media visibility of investments implemented in Wrocław, with a particular focus on the democratization of urban processes through the Wrocław Participatory Budget (WPB) and to study the public perception of these projects within the local information landscape. The paper presents an integrated analytical methodology combining geospatial data from the Spatial Information System of Wrocław (SIP) with textual data from the full corpus of local news articles from Wrocław. A hybrid data processing pipeline was used, including filtering of articles about Wrocław, geoparsing of location names, matching articles to investments using classic Term Frequency-Inverse Document Frequency (TF-IDF) models and embedding in language models such as HerBERT, and sentiment analysis using the XLM-T model. The results reveal strong imbalances in the visibility of WPB projects, that almost 90% of investments were not mentioned even once in the media. Temporal sentiment analysis indicated differences between categories of WPB projects. The results confirm the existence of “media deserts” and “islands of attention,” which leads to information exclusion for specific local communities and marginalized groups. This translates into asymmetry in residents’ knowledge of the real scope of the WPB program. The paper emphasizes the importance of Geographic Information System (GIS) fusion methods with natural language processing models (NLP) for urban research, and identifies directions for further analysis, including accompanying problems and limitations in the present day. Full article
(This article belongs to the Special Issue AI-Based Spatial Planning and Analysis)
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17 pages, 2136 KB  
Article
Study of Fair Strategy for Merchant Self-Operated Takeaway Delivery Based on Delivery Plan Optimization
by Jing Chen, Chengbo Guo, Jiahua Kou, Jiali Du, Shufa Sun and Yanling Guo
Appl. Sci. 2025, 15(12), 6650; https://doi.org/10.3390/app15126650 - 13 Jun 2025
Viewed by 887
Abstract
With the increasing demand for takeaway delivery, more merchants are developing their takeaway delivery system to manage order fulfillment and enhance the consumer online experience. This study presents a mathematical model for merchant-operated takeaway delivery, using an improved ant colony algorithm integrated with [...] Read more.
With the increasing demand for takeaway delivery, more merchants are developing their takeaway delivery system to manage order fulfillment and enhance the consumer online experience. This study presents a mathematical model for merchant-operated takeaway delivery, using an improved ant colony algorithm integrated with K-means to cluster customer locations and determine optimal routes. We propose two fairness strategies—order quantity and travel distance—to ensure equitable workload distribution among riders. The K-means algorithm is enhanced by reallocating cluster assignments based on the nodes’ distances to all cluster centers. The simulation results demonstrate that the designed algorithm and strategies generate efficient optimal delivery plans for merchants. Full article
(This article belongs to the Special Issue AI-Based Spatial Planning and Analysis)
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