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Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition)
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Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition)

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1682

Special Issue Editors

Dr. Levente Juhász

E-Mail Website
Guest Editor
Geographic Information Systems (GIS) Center, Florida International University, Miami, FL 33199, USA
Interests: user-generated spatial data; human-computer interaction; spatial databases; collaborative mapping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hartwig H. Hochmair

E-Mail Website
Guest Editor
Fort Lauderdale Research and Education Center, Geomatics Sciences, University of Florida, Fort Lauderdale, FL 33314, USA
Interests: crowd-sourced spatial data; social media; non-motorized transportation; location-based services
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Li

E-Mail Website
Guest Editor
Department of Geography, National University of Singapore, Singapore 119077, Singapore
Interests: volunteered geographic information; geospatial machine learning; multi-sensor data fusion; geo-semantics; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the integration of Artificial Intelligence (AI) with geospatial technologies has transformed the field of geodata science. Notably, the rise of Geospatial Artificial Intelligence (GeoAI) and generative AI has greatly enhanced the capabilities of traditional geospatial methods, enabling groundbreaking advancements in the mapping, extraction, generation, interpretation, and analysis of spatial information, as well as the prediction of future events or conditions. This fusion of cutting-edge AI techniques with geospatial science has provided innovative solutions to complex spatial challenges and is poised to become an integral part of everyday technologies, such as AI-Powered route optimization or AI assistants. GeoAI, with its specific focus on geospatial data and analysis, leverages machine learning, deep learning, and other AI methodologies to address problems in geospatial domains. However, several challenges remain, such as multi-scale and multi-source data integration, algorithmic bias, reproducibility, interpretability, or addressing unique spatial relationships within geospatial data.

This Special Issue builds upon the previous edition, which highlighted cutting-edge research in geospatial AI methods and their applications in areas such as mobility, natural resource and disaster management, cartography, and spatial data handling. This new Issue aims to further explore the transformative potential of AI in spatial data applications. We invite original research papers and review articles that explore the innovative use of AI for data extraction, generation, analysis, curation, and communication in the context of spatial data. While contributions demonstrating the application of AI to solve geospatial challenges are highly valued, we particularly encourage submissions that advance the field of AI itself, with a specific focus on GeoAI. Research that proposes novel methods, enhances the performance of AI algorithms, or addresses unique challenges in geospatial contexts are especially welcome.

Topics of interest may include, but are not limited to, the following:

  • Geospatial artificial intelligence (GeoAI);
  • Generative AI for geographic problems;
  • Multimodal foundation models in geographic contexts;
  • Spatial understanding, reasoning, and literacy of large language models;
  • Advanced deep learning;
  • AI assistants for mapping and cartography;
  • AI and location intelligence;
  • Machine learning and deep learning in GIS;
  • AI-based spatial data storage and transfer technologies (cloud-native geospatial, streaming, etc.);
  • Novel technologies in geocomputation and processing (serverless, edge computing, etc.)
  • Ethical, legal, and privacy considerations of GeoAI;
  • Biases in AI models;
  • Explainable AI;
  • Benchmarks and generation of datasets for AI model evaluation;
  • Bibliometric studies on selected aspects of GeoAI;
  • AI in geospatial education.

Dr. Levente Juhász
Prof. Dr. Hartwig H. Hochmair
Dr. Hao Li
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. ISPRS International Journal of Geo-Information 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 1900 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 artificial intelligence (GeoAI)
  • machine learning in GIS
  • spatial analysis
  • ethics and bias in GeoAI
  • explainable AI
  • AI for reasoning and spatial literacy
  • multimodal
  • foundation models

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

Published Papers (4 papers)

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Research

25 pages, 3610 KiB  
Article
Grid Partition-Based Dynamic Spatial–Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting
by Lifeng Gao, Liujia Chen, Agen Qiu, Qinglian Wang, Jianlong Wang, Cai Chen, Fuhao Zhang and Geli Ou’er
ISPRS Int. J. Geo-Inf. 2025, 14(5), 207; https://doi.org/10.3390/ijgi14050207 - 19 May 2025
Abstract
Accurate forecasting of city-level large-scale traffic flow is crucial for efficient traffic management and effective transport planning. However, previously proposed traffic flow prediction methods model dynamic spatial correlations across entire traffic networks, leading to high computational complexity, elevated memory usage, and model overfitting. [...] Read more.
Accurate forecasting of city-level large-scale traffic flow is crucial for efficient traffic management and effective transport planning. However, previously proposed traffic flow prediction methods model dynamic spatial correlations across entire traffic networks, leading to high computational complexity, elevated memory usage, and model overfitting. Therefore, a novel grid partition-based dynamic spatial–temporal graph convolutional network was developed in this study to capture correlations within a large-scale traffic network. It includes the following: a dynamic graph convolution module to divide the traffic network into grid regions and thereby effectively capture the local spatial dependencies inherent in large-scale traffic topologies, an attention-based dynamic graph convolutional network to capture the local spatial correlations within each region, a global spatial dependency aggregation module to model inter-regional correlation weights using sequence similarity methods and comprehensively reflect the overall state of the traffic network, and multi-scale gated convolutions to capture both long- and short-term temporal correlations across varying time ranges. The performance of the proposed model was compared with that of different baseline models using two large-scale real-world datasets; the proposed model significantly outperformed the baseline models, demonstrating its potential effectiveness in managing large-scale traffic networks. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))
26 pages, 26537 KiB  
Article
Contrastive Learning with Image Deformation and Refined NT-Xent Loss for Urban Morphology Discovery
by Chunliang Hua, Daijun Chen, Mengyuan Niu, Lizhong Gao, Junyan Yang and Qiao Wang
ISPRS Int. J. Geo-Inf. 2025, 14(5), 196; https://doi.org/10.3390/ijgi14050196 - 8 May 2025
Viewed by 266
Abstract
The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis and have been limited to the central city [...] Read more.
The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis and have been limited to the central city area. Raster analysis can lead to fragmented forms, and focusing only on the central city area ignores many representative urban forms in the suburbs and towns. In this study, a vast and complex dataset was applied to the urban morphology discovery based on the administrative community or village boundary, and a new image deformation pipeline was proposed to enhance the morphological characteristics of building groups. This allows visual neural networks to focus on extracting the morphological characteristics of building groups. Additionally, the research on urban morphology often uses unsupervised learning, which means that the learning process is difficult to control. Therefore, we refined the NT-Xent loss so that it can integrate morphological indicators. This improvement allows the visual neural network to “recognize” the similarity of samples during optimization. By defining the similarity, we can guide the network to bring samples closer or move them farther apart based on certain morphological indicators. Three Chinese cities were used for our testing. Representative urban types were identified, particularly some types located at the urban fringe. The data analysis demonstrated the effectiveness of our image deformation pipeline and loss function, and the sociological analysis illustrated the unique urban functions of these urban types. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))
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20 pages, 3506 KiB  
Article
Trajectory- and Friendship-Aware Graph Neural Network with Transformer for Next POI Recommendation
by Chenglin Yu, Lihong Shi and Yangyang Zhao
ISPRS Int. J. Geo-Inf. 2025, 14(5), 192; https://doi.org/10.3390/ijgi14050192 - 3 May 2025
Viewed by 264
Abstract
Next point-of-interest (POI) recommendation aims to predict users’ future visitation intentions based on historical check-in trajectories. However, this task faces significant challenges, including coarse-grained user interest representation, insufficient social modeling, sparse check-in data, and the insufficient learning of contextual patterns. To address this, [...] Read more.
Next point-of-interest (POI) recommendation aims to predict users’ future visitation intentions based on historical check-in trajectories. However, this task faces significant challenges, including coarse-grained user interest representation, insufficient social modeling, sparse check-in data, and the insufficient learning of contextual patterns. To address this, we propose a model that combines check-in trajectory information with user friendship relationships and uses a Transformer architecture for prediction (TraFriendFormer). Our approach begins with the construction of trajectory flow graphs using graph convolutional networks (GCNs) to globally capture POI correlations across both spatial and temporal dimensions. In parallel, we design an integrated social graph that combines explicit friendships with implicit interaction patterns, in which GraphSAGE aggregates neighborhood information to generate enriched user embeddings. Finally, we fuse the POI embeddings, user embeddings, timestamp embeddings, and category embeddings and input them into the Transformer architecture. Through the self-attention mechanism, the model captures the complex temporal relationships in the check-in sequence. We validate the effectiveness of TraFriendFormer on two real-world datasets (FourSquare and Gowalla). The experimental results show that TraFriendFormer achieves an average improvement of 10.3% to 37.2% in metrics such as Acc@k and MRR compared to the selected state-of-the-art baselines. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))
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24 pages, 2416 KiB  
Article
Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction
by Nihad Brahimi, Huaping Zhang and Zahid Razzaq
ISPRS Int. J. Geo-Inf. 2025, 14(4), 163; https://doi.org/10.3390/ijgi14040163 - 9 Apr 2025
Viewed by 388
Abstract
Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our [...] Read more.
Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our prior Unified Spatio-Temporal Inference Prediction Network (USTIN) model. It offers a comprehensive framework for the integration of various data. The eX-STIN model enhances the previous one by utilizing Ensemble Empirical Mode Decomposition (EEMD), which results in refined feature extraction. It uses Minimum Redundancy Maximum Relevance (mRMR) to find features that are relevant and not redundant, and Shapley Additive Explanations (SHAP) to show how each feature affects the model’s predictions. We conducted extensive experiments that use real car-sharing data to thoroughly evaluate the efficacy of the eX-STIN model. The studies revealed the model’s ability to accurately represent the relationships among temporal, spatial, and spatio-temporal features, outperforming the state-of-the-art models. Moreover, the experiments revealed that eX-STIN exhibits enhanced predictive accuracy compared to the USTIN model. This proposed approach enhances both the accuracy of demand prediction and the transparency of resource allocation decisions in car-sharing services. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))
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