ISPRS International Journal of Geo-Information

19 pages, 2936 KB

Open AccessArticle

Machine Learning-Based Identification of Key Predictors for Lightning Events in the Third Pole Region

by Harshwardhan Jadhav, Prashant Singh, Bodo Ahrens and Juerg Schmidli

ISPRS Int. J. Geo-Inf. 2025, 14(8), 319; https://doi.org/10.3390/ijgi14080319 - 21 Aug 2025

Viewed by 605

The Third Pole region, particularly the Hindu–Kush–Himalaya (HKH), is highly prone to lightning, causing thousands of fatalities annually. Skillful prediction and timely communication are essential for mitigating lightning-related losses in such observationally data-sparse regions. Therefore, this study evaluates kilometer-scale ICON-CLM-simulated atmospheric variables using [...] Read more.

The Third Pole region, particularly the Hindu–Kush–Himalaya (HKH), is highly prone to lightning, causing thousands of fatalities annually. Skillful prediction and timely communication are essential for mitigating lightning-related losses in such observationally data-sparse regions. Therefore, this study evaluates kilometer-scale ICON-CLM-simulated atmospheric variables using six machine learning (ML) models to detect lightning activity over the Third Pole. Results from the ensemble boosting ML models show that ICON-CLM simulated variables such as relative humidity (RH), vorticity (vor), 2m temperature (t_2m), and surface pressure (sfc_pres) among a total of 25 variables allow better spatial and temporal prediction of lightning activities, achieving a Probability of Detection (POD) of ∼0.65. The Lightning Potential Index (LPI) and the product of convective available potential energy (CAPE) and precipitation (prec_con), referred to as CP (i.e., CP = CAPE × precipitation), serve as key physics aware predictors, maintaining a high Probability of Detection (POD) of ∼0.62 with a 1–2 h lead time. Sensitivity analyses additionally using climatological lightning data showed that while ML models maintain comparable accuracy and POD, climatology primarily supports broad spatial patterns rather than fine-scale prediction improvements. As LPI and CP reflect cloud microphysics and atmospheric stability, their inclusion, along with spatiotemporal averaging and climatology, offers slightly lower, yet comparable, predictive skill to that achieved by aggregating 25 atmospheric predictors. Model evaluation using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) highlights XGBoost as the best-performing diagnostic classification (yes/no lightning) model across all six ML tested configurations. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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21 pages, 2347 KB

Open AccessArticle

Digital Twins and Big Data in the Metaverse: Addressing Privacy, Scalability, and Interoperability with AI and Blockchain

by Ruoxuan Li, Hemn Barzan Abdalla, Mehdi Gheisari and Hamidreza Rabiei-Dastjerdi

ISPRS Int. J. Geo-Inf. 2025, 14(8), 318; https://doi.org/10.3390/ijgi14080318 - 20 Aug 2025

Viewed by 742

Abstract

This paper explores the integration of digital twin technologies and big data in the metaverse to improve urban traffic management. It highlights the importance of technology in mirroring and augmenting our physical and virtual worlds. This study examines how big data and digital [...] Read more.

This paper explores the integration of digital twin technologies and big data in the metaverse to improve urban traffic management. It highlights the importance of technology in mirroring and augmenting our physical and virtual worlds. This study examines how big data and digital twin technologies merge in the metaverse to improve traffic management. Our work applies artificial intelligence (AI) and blockchain technologies to address concerns about privacy, scalability, and interoperability. In a literature review and case study on traffic management, we outline how big data analytics and digital twins can increase operational and decision-making efficiency. This study aims to elucidate the transformative potential of such technologies for urban transport and postulates future areas of social, regulatory, and environmental research gaps. Full article

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24 pages, 4012 KB

Open AccessArticle

Copyright Protection and Trusted Transactions for 3D Models Based on Smart Contracts and Zero-Watermarking

by Ruigang Nan, Liming Zhang, Jianing Xie, Yan Jin, Tao Tan, Shuaikang Liu and Haoran Wang

ISPRS Int. J. Geo-Inf. 2025, 14(8), 317; https://doi.org/10.3390/ijgi14080317 - 20 Aug 2025

Viewed by 526

Abstract

With the widespread application of 3D models derived from oblique photography, the need for copyright protection and trusted transactions has risen significantly. Traditional transactions often depend on third parties, making it difficult to balance copyright protection with transaction credibility and to safeguard the [...] Read more.

With the widespread application of 3D models derived from oblique photography, the need for copyright protection and trusted transactions has risen significantly. Traditional transactions often depend on third parties, making it difficult to balance copyright protection with transaction credibility and to safeguard the rights and interests of both parties. To address these challenges, this paper proposes a novel trusted-transaction scheme that integrates smart contracts with zero-watermarking technology. Firstly, the skewness of the oblique-photography 3D model data is employed to construct a zero-watermark identifier, which is stored in the InterPlanetary File System (IPFS) alongside encrypted data for trading. Secondly, smart contracts are designed and deployed. Lightweight information, such as IPFS data addresses, is uploaded to the blockchain by invoking these contracts, and transactions are conducted accordingly. Finally, the blockchain system automatically records the transaction process and results on-chain, providing verifiable transaction evidence. The experimental results show that the proposed zero-watermarking algorithm resists common attacks. The trusted-transaction framework not only ensures the traceability and trustworthiness of the entire transaction process but also safeguards the rights of both parties. This approach effectively protects copyright while ensuring the reliability of the transactions. Full article

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18 pages, 252 KB

Open AccessArticle

Navigating the Pandemic with GIS: An Exploratory Factor Analysis of Israel’s Municipal Response

by Shimon Fridkin, Gil Greenstein, Diana Levi and Evgenia Tamurov

ISPRS Int. J. Geo-Inf. 2025, 14(8), 316; https://doi.org/10.3390/ijgi14080316 - 19 Aug 2025

Viewed by 659

Abstract

This study examined the role of Geographic Information Systems (GIS) in municipal responses to the COVID-19 pandemic in Israel. A structured survey of officials from 130 municipalities was conducted, with a focus on the 87 municipalities that utilized GIS. An Exploratory Factor Analysis [...] Read more.

This study examined the role of Geographic Information Systems (GIS) in municipal responses to the COVID-19 pandemic in Israel. A structured survey of officials from 130 municipalities was conducted, with a focus on the 87 municipalities that utilized GIS. An Exploratory Factor Analysis (EFA) was performed on the survey data from these GIS-user municipalities to identify the underlying dimensions of GIS application during the crisis. The analysis revealed that municipal GIS engagement is not a monolithic activity but is composed of three distinct, reliable, and interpretable latent factors: (1) Strategic and Operational Integration, reflecting the deep embedding of GIS into core governance and decision-making; (2) Temporal Engagement, capturing the sustained use of the system over the timeline of the pandemic’s fourth wave; and (3) Logistical Site Coordination, representing the specialized use of GIS for managing testing and vaccination sites. These findings move beyond documenting individual GIS tasks to provide an empirical, data-driven framework of how geospatial technology was operationalized. This study underscores the multidimensional nature of GIS in a public health emergency and offers a structured understanding that can inform future crisis preparedness, training, and technology implementation strategies for municipal governments. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

22 pages, 9020 KB

Open AccessArticle

Towards Transparent Urban Perception: A Concept-Driven Framework with Visual Foundation Models

by Yixin Yu, Zepeng Yu, Xuhua Shi, Ran Wan, Bowen Wang and Jiaxin Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(8), 315; https://doi.org/10.3390/ijgi14080315 - 18 Aug 2025

Viewed by 582

Abstract

Understanding urban visual perception is crucial for modeling how individuals cognitively and emotionally interact with the built environment. However, traditional survey-based approaches are limited in scalability and often fail to generalize across diverse urban contexts. In this study, we introduce the UP-CBM, a [...] Read more.

Understanding urban visual perception is crucial for modeling how individuals cognitively and emotionally interact with the built environment. However, traditional survey-based approaches are limited in scalability and often fail to generalize across diverse urban contexts. In this study, we introduce the UP-CBM, a transparent framework that leverages visual foundation models (VFMs) and concept-based reasoning to address these challenges. The UP-CBM automatically constructs a task-specific vocabulary of perceptual concepts using GPT-4o and processes urban scene images through a multi-scale visual prompting pipeline. This pipeline generates CLIP-based similarity maps that facilitate the learning of an interpretable bottleneck layer, effectively linking visual features with human perceptual judgments. Our framework not only achieves higher predictive accuracy but also offers enhanced interpretability, enabling transparent reasoning about urban perception. Experiments on two benchmark datasets—Place Pulse 2.0 (achieving improvements of +0.041 in comparison accuracy and +0.029 in

R^{2}

) and VRVWPR (+0.018 in classification accuracy)—demonstrate the effectiveness and generalizability of our approach. These results underscore the potential of integrating VFMs with structured concept-driven pipelines for more explainable urban visual analytics. Full article

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21 pages, 1339 KB

Open AccessArticle

Generative AI for Geospatial Analysis: Fine-Tuning ChatGPT to Convert Natural Language into Python-Based Geospatial Computations

by Zachary Sherman, Sandesh Sharma Dulal, Jin-Hee Cho, Mengxi Zhang and Junghwan Kim

ISPRS Int. J. Geo-Inf. 2025, 14(8), 314; https://doi.org/10.3390/ijgi14080314 - 18 Aug 2025

Viewed by 1233

Abstract

This study investigates the potential of fine-tuned large language models (LLMs) to enhance geospatial intelligence by translating natural language queries into executable Python code. Traditional GIS workflows, while effective, often lack usability and scalability for non-technical users. LLMs offer a new approach by [...] Read more.

This study investigates the potential of fine-tuned large language models (LLMs) to enhance geospatial intelligence by translating natural language queries into executable Python code. Traditional GIS workflows, while effective, often lack usability and scalability for non-technical users. LLMs offer a new approach by enabling conversational interaction with spatial data. We evaluate OpenAI’s GPT-4o-mini model in two forms: an “As-Is” baseline and a fine-tuned version trained on 600+ prompt–response pairs related to geospatial Python scripting in Virginia. Using U.S. Census shapefiles and hospital data, we tested both models across six types of spatial queries. The fine-tuned model achieved 89.7%, a 49.2 percentage point improvement over the baseline’s 40.5%. It also demonstrated substantial reductions in execution errors and token usage. Key innovations include the integration of spatial reasoning, modular external function calls, and fuzzy geographic input correction. These findings suggest that fine-tuned LLMs can improve the accuracy, efficiency, and usability of geospatial dashboards when they are powered by LLMs. Our results further imply a scalable and replicable approach for future domain-specific AI applications in geospatial science and smart cities studies. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))

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13 pages, 2011 KB

Open AccessArticle

GeoAI in Topographic Mapping: Navigating the Future of Opportunities and Risks

by Bala Bhavya Kausika and Vincent van Altena

ISPRS Int. J. Geo-Inf. 2025, 14(8), 313; https://doi.org/10.3390/ijgi14080313 - 17 Aug 2025

Viewed by 1095

Abstract

Geospatial Artificial Intelligence (GeoAI) has been advancing and altering geographic information systems and Earth observation by enhancing the computation and understanding capabilities of these systems. In this context, the application of GeoAI in topographic mapping presents a transformative opportunity for national mapping agencies [...] Read more.

Geospatial Artificial Intelligence (GeoAI) has been advancing and altering geographic information systems and Earth observation by enhancing the computation and understanding capabilities of these systems. In this context, the application of GeoAI in topographic mapping presents a transformative opportunity for national mapping agencies worldwide. While GeoAI offers significant advantages, its adoption can also introduce new challenges, necessitating organization-wide transformations for sustainable implementation. Opportunities in the future of topographic mapping include improved data processing and real-time mapping capabilities. However, the adoption of GeoAI also brings forth various risks, including data privacy concerns, algorithmic biases, and the need for robust cybersecurity measures, which are pivotal to the national mapping organizations. Given the rapid technological advancements in AI and computing, and the challenges that national mapping agencies currently face, we discuss the potential opportunities and risks of GeoAI from a multi-perspective view. By examining global examples and trends, and synthesizing insights from current applications and theoretical frameworks, this paper aims to provide a comprehensive overview of GeoAI’s impact on topographic mapping within the context of national mapping, offering strategic recommendations for stakeholders to leverage opportunities while mitigating risks. Full article

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21 pages, 6043 KB

Open AccessArticle

Identification of Abandoned Tea Lands in Kandy District, Sri Lanka Using Trajectory Analysis and Satellite Remote Sensing

by Sirantha Jagath Kumara Athauda and Takehiro Morimoto

ISPRS Int. J. Geo-Inf. 2025, 14(8), 312; https://doi.org/10.3390/ijgi14080312 - 15 Aug 2025

Viewed by 795

Abstract

Tea is a prominent cash crop in global agriculture, and it is Sri Lanka’s top agricultural export known as ‘Ceylon Tea,’ employing nearly one million people, with land covering an area of 267,000 ha. However, over the past decade, many tea lands in [...] Read more.

Tea is a prominent cash crop in global agriculture, and it is Sri Lanka’s top agricultural export known as ‘Ceylon Tea,’ employing nearly one million people, with land covering an area of 267,000 ha. However, over the past decade, many tea lands in Sri Lanka have been abandoned, leading to a gradual decline in production. This research aims to identify, map, and verify tea land abandonment over time and space by identifying and analyzing a series of land use trajectories with Landsat, Google Earth, and PlanetScope imageries to provide a substantial knowledge base. The study area covers five Divisional Secretariats Divisions in Kandy District, Central Highlands of Sri Lanka: Delthota, Doluwa, Udapalatha, Ganga Ihala Korale, and Pasbage Korale, where around 70% of the tea lands in Kandy District are covered. Six land use/cover (LULC) classes were considered: tea, Home Garden and Other Crop, forest, grass and bare land, built-up area, and Water Body. Abandoned tea lands were identified if the tea land was converted to another land use between 2015 and 2023. The results revealed the following: (1) 85% accuracy in LULC classification, revealing tea as the second-largest land use. Home Garden and Other Crop dominated, with an expanding built-up area. (2) The top 22 trajectories dominating the tea trajectories were identified, indicating that tea abandonment peaked between 2017 and 2023. (3) In total, 12% (5457 ha) of pixels were identified as abandoned tea lands during the observation period (2015–2023) at an accuracy rate of 94.7% in the validation. Significant changes were observed between the two urban centers of Gampola and Nawalapitiya towns. (4) Tea land abandonment over 7 years was the highest at 35% (1892.3 ha), while 5-year and 3-year periods accounted for 535.4 ha and 353.6 ha, respectively, highlighting a significant long-term trend. (5) The predominant conversion observed is the shift in tea towards Home Garden and Other Crop (2986.2 ha) during the timeframe. The findings underscore the extent and dynamics of tea land abandonment, providing critical insights into the patterns and characteristics of abandoned lands. This study fills a key research gap by offering a comprehensive spatial analysis of tea land abandonment in Sri Lanka. The results are valuable for stakeholders in the tea industry, providing essential information for sustainable management, policy-making, and future research on the spatial factors driving tea land abandonment. Full article

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21 pages, 9031 KB

Open AccessArticle

A Pyramid Convolution-Based Scene Coordinate Regression Network for AR-GIS

by Haobo Xu, Chao Zhu, Yilong Wang, Huachen Zhu and Wei Ma

ISPRS Int. J. Geo-Inf. 2025, 14(8), 311; https://doi.org/10.3390/ijgi14080311 - 15 Aug 2025

Viewed by 626

Abstract

Camera tracking plays a pivotal role in augmented reality geographic information systems (AR-GIS) and location-based services (LBS), serving as a crucial component for accurate spatial awareness and navigation. Current learning-based camera tracking techniques, while achieving superior accuracy in pose estimation, often overlook changes [...] Read more.

Camera tracking plays a pivotal role in augmented reality geographic information systems (AR-GIS) and location-based services (LBS), serving as a crucial component for accurate spatial awareness and navigation. Current learning-based camera tracking techniques, while achieving superior accuracy in pose estimation, often overlook changes in scale. This oversight results in less stable localization performance and challenges in coping with dynamic environments. To address these tasks, we propose a pyramid convolution-based scene coordinate regression network (PSN). Our approach leverages a pyramidal convolutional structure, integrating kernels of varying sizes and depths, alongside grouped convolutions that alleviate computational demands while capturing multi-scale features from the input imagery. Subsequently, the network incorporates a novel randomization strategy, effectively diminishing correlated gradients and markedly bolstering the training process’s efficiency. The culmination lies in a regression layer that maps the 2D pixel coordinates to their corresponding 3D scene coordinates with precision. The experimental outcomes show that our proposed method achieves centimeter-level accuracy in small-scale scenes and decimeter-level accuracy in large-scale scenes after only a few minutes of training. It offers a favorable balance between localization accuracy and efficiency, and effectively supports augmented reality visualization in dynamic environments. Full article

(This article belongs to the Topic Simulations and Applications of Augmented and Virtual Reality, 2nd Edition)

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6 pages, 193 KB

Open AccessEditorial

Recent Progress in and Future Perspectives on the Monitoring, Assessment, and Mitigation of Geological Disasters

by Yan Du, Mengjia Lyu, Mowen Xie, Yujing Jiang, Bo Li and Xuepeng Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(8), 310; https://doi.org/10.3390/ijgi14080310 - 13 Aug 2025

Viewed by 727

Abstract

Among all the geological disasters (GDs), collapses, landslides, and debris flows are the most severe [...] Full article

(This article belongs to the Topic Geotechnics for Hazard Mitigation)

23 pages, 14091 KB

Open AccessFeature PaperArticle

New Sampling Method for Landslide Susceptibility Evaluation with Consideration of Minimizing Potential Societal Losses

by Zhao Lu, Yu Chen, Yongming Wei, Yufei Zhang and Xianfeng Cheng

ISPRS Int. J. Geo-Inf. 2025, 14(8), 309; https://doi.org/10.3390/ijgi14080309 - 13 Aug 2025

Viewed by 583

Abstract

In landslide susceptibility evaluation, scientific sampling minimizes potential societal losses and enhances the efficiency of disaster prevention and mitigation. However, traditional sampling methods, such as selecting landslide and non-landslide samples based on equal proportions or area proportions, overlook the different societal losses resulting [...] Read more.

In landslide susceptibility evaluation, scientific sampling minimizes potential societal losses and enhances the efficiency of disaster prevention and mitigation. However, traditional sampling methods, such as selecting landslide and non-landslide samples based on equal proportions or area proportions, overlook the different societal losses resulting from landslide omission and misreporting, and the potential societal losses faced by their evaluation results are often not minimized. Therefore, this study proposes a sampling method that takes potential societal losses into account and uses the Landslide Misjudgment Potential Societal Loss Evaluation Index (LMPSLEI) to quantify the total potential social losses in the area due to landslide omission and misreporting. The LMPSLEI is minimized by optimizing the sample ratio, thus minimizing the potential societal losses faced by the evaluation results and enhancing the scientific basis of disaster prevention and mitigation efforts. This study takes the Wenchuan earthquake area as the research region, selects 13 conditional factors and employs two models—Random Forest (RF) and Convolutional Neural Network (CNN)—to conduct case studies. We derive the recommended sample ratio based on the formula, hypothesizing that the LMPSLEI will be minimized under this ratio. The results show that the sample ratio for LMPSLEI minimization in the RF model is similar to the recommended sample ratio, while the sample ratio for LMPSLEI minimization in the CNN model is slightly higher than the recommended sample ratio. The recommended sample ratio can achieve the minimum of LMPSLEI or reach a lower value under different societal losses weights of landslide omission/misreporting, and thus it can be used as a preliminary choice of sampling for landslide susceptibility evaluation considering the potential societal losses. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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22 pages, 9411 KB

Open AccessArticle

A Spatiotemporal Multi-Model Ensemble Framework for Urban Multimodal Traffic Flow Prediction

by Zhenkai Wang and Lujin Hu

ISPRS Int. J. Geo-Inf. 2025, 14(8), 308; https://doi.org/10.3390/ijgi14080308 - 10 Aug 2025

Viewed by 1014

Abstract

Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, and buses. To address the limitations of existing methods in capturing these [...] Read more.

Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, and buses. To address the limitations of existing methods in capturing these diverse trajectory characteristics, we propose a spatiotemporal multi-model ensemble framework, which is an ensemble model called GLEN (GCN and LSTM Ensemble Network). Firstly, the trajectory feature adaptive driven model selection mechanism classifies trajectories into dynamic travel and fixed-route scenarios. Secondly, we use a Graph Convolutional Network (GCN) to capture dynamic travel patterns and Long Short-Term Memory (LSTM) network to model fixed-route patterns. Subsequently the outputs of these models are dynamically weighted, integrated, and fused over a spatiotemporal grid to produce accurate forecasts of urban total traffic flow at multiple future time steps. Finally, experimental validation using Beijing’s Chaoyang district datasets demonstrates that our framework effectively captures spatiotemporal and interactive characteristics between multimodal travel trajectories and outperforms mainstream baselines, thereby offering robust support for urban traffic management and planning. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))

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25 pages, 6136 KB

Open AccessArticle

Bridging Humanitarian Mapping and the Sustainable Development Goals

by Quang Huy Nguyen, Maria Antonia Brovelli, Alberta Albertella, Taichi Furuhashi and Michael Montani

ISPRS Int. J. Geo-Inf. 2025, 14(8), 307; https://doi.org/10.3390/ijgi14080307 - 8 Aug 2025

Viewed by 1227

Abstract

The Sustainable Development Goals (SDGs) have become the global framework for evaluating the effectiveness of humanitarian projects. Humanitarian mapping is considered a popular voluntary geographic information technique that provides data for disaster response. Although humanitarian mapping has contributed significantly to the SDGs, there [...] Read more.

The Sustainable Development Goals (SDGs) have become the global framework for evaluating the effectiveness of humanitarian projects. Humanitarian mapping is considered a popular voluntary geographic information technique that provides data for disaster response. Although humanitarian mapping has contributed significantly to the SDGs, there is a lack of in-depth studies on the state of this relationship. This paper aims to assess the potential relationship between the SDGs and humanitarian mapping by (1) analyzing SDG indicators to determine their potential contribution to humanitarian mapping, and (2) identifying the actual contribution of humanitarian mapping projects to the SDGs. To achieve this, the study uses a structured methodology that combines SDG indicator analysis with project-level data filtering and text mining. Three major humanitarian mapping platforms—HOT-TM, MapSwipe, and Ushahidi—are examined in order to capture their potential and actual contributions to the SDG framework. Ultimately, the study highlights the strong alignment between humanitarian mapping activities and the need to monitor the SDGs, particularly in water, urban infrastructure, and land use, emphasizing the potential of volunteer-driven geospatial data to address critical data gaps. Full article

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22 pages, 639 KB

Open AccessArticle

Variations on the Theme “Definition of the Orthodrome”

by Miljenko Lapaine

ISPRS Int. J. Geo-Inf. 2025, 14(8), 306; https://doi.org/10.3390/ijgi14080306 - 6 Aug 2025

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Abstract

A geodesic or geodetic line on a sphere is called the orthodrome. Research has shown that the orthodrome can be defined in a large number of ways. This article provides an overview of various definitions of the orthodrome. We recall the definitions of [...] Read more.

A geodesic or geodetic line on a sphere is called the orthodrome. Research has shown that the orthodrome can be defined in a large number of ways. This article provides an overview of various definitions of the orthodrome. We recall the definitions of the orthodrome according to the greats of geodesy, such as Bessel and Helmert. We derive the equation of the orthodrome in the geographic coordinate system and in the Cartesian spatial coordinate system. A geodesic on a surface is a curve for which the geodetic curvature is zero at every point. Equivalent expressions of this statement are that at every point of this curve, the principal normal vector is collinear with the normal to the surface, i.e., it is a curve whose binormal at every point is perpendicular to the normal to the surface, and that it is a curve whose osculation plane contains the normal to the surface at every point. In this case, the well-known Clairaut equation of the geodesic in geodesy appears naturally. It is found that this equation can be written in several different forms. Although differential equations for geodesics can be found in the literature, they are solved in this article, first, by taking the sphere as a special case of any surface, and then as a special case of a surface of rotation. At the end of this article, we apply calculus of variations to determine the equation of the orthodrome on the sphere, first in the Bessel way, and then by applying the Euler–Lagrange equation. Overall, this paper elaborates a dozen different approaches to orthodrome definitions. Full article

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19 pages, 4722 KB

Open AccessArticle

Effect of Dynamic Point Symbol Visual Coding on User Search Performance in Map-Based Visualizations

by Weijia Ge, Jing Zhang, Xingjian Shi, Wenzhe Tang and Longlong Qian

ISPRS Int. J. Geo-Inf. 2025, 14(8), 305; https://doi.org/10.3390/ijgi14080305 - 5 Aug 2025

Viewed by 589

Abstract

As geographic information visualization continues to gain prominence, dynamic symbols are increasingly employed in map-based applications. However, the optimal visual coding for dynamic point symbols—particularly concerning encoding type, animation rate, and modulation area—remains underexplored. This study examines how these factors influence user performance [...] Read more.

As geographic information visualization continues to gain prominence, dynamic symbols are increasingly employed in map-based applications. However, the optimal visual coding for dynamic point symbols—particularly concerning encoding type, animation rate, and modulation area—remains underexplored. This study examines how these factors influence user performance in visual search tasks through two eye-tracking experiments. Experiment 1 investigated the effects of two visual coding factors: encoding types (flashing, pulsation, and lightness modulation) and animation rates (low, medium, and high). Experiment 2 focused on the interaction between encoding types and modulation areas (fill, contour, and entire symbol) under a fixed animation rate condition. The results revealed that search performance deteriorates as the animation rate of the fastest target symbol exceeds 10 fps. Flashing and lightness modulation outperformed pulsation, and modulation areas significantly impacted efficiency and accuracy, with notable interaction effects. Based on the experimental results, three visual coding strategies are recommended for optimal performance in map-based interfaces: contour pulsation, contour flashing, and entire symbol lightness modulation. These findings provide valuable insights for optimizing the design of dynamic point symbols, contributing to improved user engagement and task performance in cartographic and geovisual applications. Full article

(This article belongs to the Topic Theories and Applications of Human-Computer Interaction)

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34 pages, 7266 KB

Open AccessArticle

Relationship Between Aggregation Index and Change in the Values of Some Landscape Metrics as a Function of Cell Neighborhood Choice

by Paolo Zatelli, Clara Tattoni and Marco Ciolli

ISPRS Int. J. Geo-Inf. 2025, 14(8), 304; https://doi.org/10.3390/ijgi14080304 - 5 Aug 2025

Viewed by 554

Abstract

Landscape metrics are one of the main tools for studying changes in the landscape and the ecological structure of the territory. However, the calculation of some metrics yields significantly different values depending on the configuration of the “Cell neighborhood” (CN) used. This makes [...] Read more.

Landscape metrics are one of the main tools for studying changes in the landscape and the ecological structure of the territory. However, the calculation of some metrics yields significantly different values depending on the configuration of the “Cell neighborhood” (CN) used. This makes the comparison of different analysis results often impossible. In fact, although the metrics are defined in the same way for all software, the choice of a CN with four cells, which includes only the elements on the same row or column, or eight cells, which also includes the cells on the diagonal, changes their value. QGIS’ LecoS plugin uses the value eight while GRASS’ r.li module uses the value four and these values are not modifiable by users. A previous study has shown how the value of the CN used for the calculation of landscape metrics is rarely explicit in scientific publications and its value cannot always be deduced from the indication of the software used. The difference in value for the same metric depends on the CN configuration and on the compactness of the patches, which can be expressed through the Aggregation Index (AI), of the investigated landscape. The scope of this paper is to explore the possibility of deriving an analytical relationship between the Aggregation Index and the variation in the values of some landscape metrics as the CN varies. The numerical experiments carried out in this research demonstrate that it is possible to estimate the differences in landscape metrics evaluated with a four and eight CN configuration using polynomials only for few metrics and only for some intervals of AI values. This analysis combines different Free and Open Source Software (FOSS) systems: GRASS GIS for the creation of test maps and R landscapemetrics package for the calculation of landscape metrics and the successive statistical analysis. Full article

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23 pages, 15241 KB

Open AccessArticle

Diffusion Model-Based Cartoon Style Transfer for Real-World 3D Scenes

by Yuhang Chen, Haoran Zhou, Jing Chen, Nai Yang, Jing Zhao and Yi Chao

ISPRS Int. J. Geo-Inf. 2025, 14(8), 303; https://doi.org/10.3390/ijgi14080303 - 4 Aug 2025

Viewed by 1026

Abstract

Traditional map style transfer methods are mostly based on GAN, which are either overly artistic at the expense of conveying information, or insufficiently aesthetic by simply changing the color scheme of the map image. These methods often struggle to balance style transfer with [...] Read more.

Traditional map style transfer methods are mostly based on GAN, which are either overly artistic at the expense of conveying information, or insufficiently aesthetic by simply changing the color scheme of the map image. These methods often struggle to balance style transfer with semantic preservation and lack consistency in their transfer effects. In recent years, diffusion models have made significant progress in the field of image processing and have shown great potential in image-style transfer tasks. Inspired by these advances, this paper presents a method for transferring real-world 3D scenes to a cartoon style without the need for additional input condition guidance. The method combines pre-trained LDM with LoRA models to achieve stable and high-quality style infusion. By integrating DDIM Inversion, ControlNet, and MultiDiffusion strategies, it achieves the cartoon style transfer of real-world 3D scenes through initial noise control, detail redrawing, and global coordination. Qualitative and quantitative analyses, as well as user studies, indicate that our method effectively injects a cartoon style while preserving the semantic content of the real-world 3D scene, maintaining a high degree of consistency in style transfer. This paper offers a new perspective for map style transfer. Full article

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27 pages, 9910 KB

Open AccessArticle

Predicting the Next Location of Urban Individuals via a Representation-Enhanced Multi-View Learning Network

by Maoqi Lun, Peixiao Wang, Sheng Wu, Hengcai Zhang, Shifen Cheng and Feng Lu

ISPRS Int. J. Geo-Inf. 2025, 14(8), 302; https://doi.org/10.3390/ijgi14080302 - 2 Aug 2025

Viewed by 645

Abstract

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. [...] Read more.

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))

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19 pages, 7359 KB

Open AccessArticle

An Aspect-Based Emotion Analysis Approach on Wildfire-Related Geo-Social Media Data—A Case Study of the 2020 California Wildfires

by Christina Zorenböhmer, Shaily Gandhi, Sebastian Schmidt and Bernd Resch

ISPRS Int. J. Geo-Inf. 2025, 14(8), 301; https://doi.org/10.3390/ijgi14080301 - 1 Aug 2025

Viewed by 656

Abstract

Natural disasters like wildfires pose significant threats to communities, which necessitates timely and effective disaster response strategies. While Aspect-based Sentiment Analysis (ABSA) has been widely used to extract sentiment-related information at the sub-sentence level, the corresponding field of Aspect-based Emotion Analysis (ABEA) remains [...] Read more.

Natural disasters like wildfires pose significant threats to communities, which necessitates timely and effective disaster response strategies. While Aspect-based Sentiment Analysis (ABSA) has been widely used to extract sentiment-related information at the sub-sentence level, the corresponding field of Aspect-based Emotion Analysis (ABEA) remains underexplored due to dataset limitations and the increased complexity of emotion classification. In this study, we used EmoGRACE, a fine-tuned BERT-based model for ABEA, which we applied to georeferenced tweets of the 2020 California wildfires. The results for this case study reveal distinct spatio-temporal emotion patterns for wildfire-related aspect terms, with fear and sadness increasing near wildfire perimeters. This study demonstrates the feasibility of tracking emotion dynamics across disaster-affected regions and highlights the potential of ABEA in real-time disaster monitoring. The results suggest that ABEA can provide a nuanced understanding of public sentiment during crises for policymakers. Full article

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19 pages, 12406 KB

Open AccessArticle

Optimizing Advertising Billboard Coverage in Urban Networks: A Population-Weighted Greedy Algorithm with Spatial Efficiency Enhancements

by Jiaying Fu and Kun Qin

ISPRS Int. J. Geo-Inf. 2025, 14(8), 300; https://doi.org/10.3390/ijgi14080300 - 1 Aug 2025

Viewed by 561

Abstract

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and [...] Read more.

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article

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27 pages, 7810 KB

Open AccessArticle

Mutation Interval-Based Segment-Level SRDet: Side Road Detection Based on Crowdsourced Trajectory Data

by Ying Luo, Fengwei Jiao, Longgang Xiang, Xin Chen and Meng Wang

ISPRS Int. J. Geo-Inf. 2025, 14(8), 299; https://doi.org/10.3390/ijgi14080299 - 31 Jul 2025

Viewed by 525

Abstract

Accurate side road detection is essential for traffic management, urban planning, and vehicle navigation. However, existing research mainly focuses on road network construction, lane extraction, and intersection identification, while fine-grained side road detection remains underexplored. Therefore, this study proposes a road segment-level side [...] Read more.

Accurate side road detection is essential for traffic management, urban planning, and vehicle navigation. However, existing research mainly focuses on road network construction, lane extraction, and intersection identification, while fine-grained side road detection remains underexplored. Therefore, this study proposes a road segment-level side road detection method based on crowdsourced trajectory data: First, considering the geometric and dynamic characteristics of trajectories, SRDet introduces a trajectory lane-change pattern recognition method based on mutation intervals to distinguish the heterogeneity of lane-change behaviors between main and side roads. Secondly, combining geometric features with spatial statistical theory, SRDet constructs multimodal features for trajectories and road segments, and proposes a potential side road segment classification model based on random forests to achieve precise detection of side road segments. Finally, based on mutation intervals and potential side road segments, SRDet utilizes density peak clustering to identify main and side road access points, completing the fitting of side roads. Experiments were conducted using 2021 Beijing trajectory data. The results show that SRDet achieves precision and recall rates of 84.6% and 86.8%, respectively. This demonstrates the superior performance of SRDet in side road detection across different areas, providing support for the precise updating of urban road navigation information. Full article

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21 pages, 2555 KB

Open AccessArticle

Statistical Depth Measures in Density-Based Clustering with Automatic Adjustment for Skewed Data

by Mark McKenney and Daniel Tucek

ISPRS Int. J. Geo-Inf. 2025, 14(8), 298; https://doi.org/10.3390/ijgi14080298 - 30 Jul 2025

Viewed by 487

Abstract

Statistical data depth measures have been applied to density-based clustering techniques in an effort to achieve robustness in parameter selection via the affine invariant property of the depth measure. Specifically, the Mahalanobis depth measure is used in the application of DBSCAN. In this [...] Read more.

Statistical data depth measures have been applied to density-based clustering techniques in an effort to achieve robustness in parameter selection via the affine invariant property of the depth measure. Specifically, the Mahalanobis depth measure is used in the application of DBSCAN. In this paper, we examine properties of the Mahalanobis depth measure that lead to instances where it fails to detect clusters in DBSCAN, whereas Euclidean distance is able to differentiate the clusters. We propose two solutions to the problems induced by these properties. The first re-examines clusters to determine if data shape is causing multiple clusters to be grouped into a single cluster. The second examines the use of a different measure as an alternate depth function. Experiments are provided. Full article

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23 pages, 7371 KB

Open AccessArticle

A Novel Method for Estimating Building Height from Baidu Panoramic Street View Images

by Shibo Ge, Jiping Liu, Xianghong Che, Yong Wang and Haosheng Huang

ISPRS Int. J. Geo-Inf. 2025, 14(8), 297; https://doi.org/10.3390/ijgi14080297 - 30 Jul 2025

Viewed by 741

Abstract

Building height information plays an important role in many urban-related applications, such as urban planning, disaster management, and environmental studies. With the rapid development of real scene maps, street view images are becoming a new data source for building height estimation, considering their [...] Read more.

Building height information plays an important role in many urban-related applications, such as urban planning, disaster management, and environmental studies. With the rapid development of real scene maps, street view images are becoming a new data source for building height estimation, considering their easy collection and low cost. However, existing studies on building height estimation primarily utilize remote sensing images, with little exploration of height estimation from street-view images. In this study, we proposed a deep learning-based method for estimating the height of a single building in Baidu panoramic street view imagery. Firstly, the Segment Anything Model was used to extract the region of interest image and location features of individual buildings from the panorama. Subsequently, a cross-view matching algorithm was proposed by combining Baidu panorama and building footprint data with height information to generate building height samples. Finally, a Two-Branch feature fusion model (TBFF) was constructed to combine building location features and visual features, enabling accurate height estimation for individual buildings. The experimental results showed that the TBFF model had the best performance, with an RMSE of 5.69 m, MAE of 3.97 m, and MAPE of 0.11. Compared with two state-of-the-art methods, the TBFF model exhibited robustness and higher accuracy. The Random Forest model had an RMSE of 11.83 m, MAE of 4.76 m, and MAPE of 0.32, and the Pano2Geo model had an RMSE of 10.51 m, MAE of 6.52 m, and MAPE of 0.22. The ablation analysis demonstrated that fusing building location and visual features can improve the accuracy of height estimation by 14.98% to 69.99%. Moreover, the accuracy of the proposed method meets the LOD1 level 3D modeling requirements defined by the OGC (height error ≤ 5 m), which can provide data support for urban research. Full article

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22 pages, 3025 KB

Open AccessArticle

Exploring the Spatial Association Between Spatial Categorical Data Using a Fuzzy Geographically Weighted Colocation Quotient Method

by Ling Li, Lian Duan, Meiyi Li and Xiongfa Mai

ISPRS Int. J. Geo-Inf. 2025, 14(8), 296; https://doi.org/10.3390/ijgi14080296 - 29 Jul 2025

Viewed by 564

Abstract

Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to [...] Read more.

Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to define the scale effect, which can lead to scale sensitivity and discontinuity results. To address these limitations, this study introduces the Fuzzy Geographically Weighted Colocation Quotient (FGWCLQ) method. By integrating fuzzy theory, FGWCLQ replaces binary distance cutoffs with continuous membership functions, providing a more flexible and stable approach to spatial association mining. Using Point of Interest (POI) data from the Beijing urban area, FGWCLQ was applied to explore both intra- and inter-category spatial association patterns among star hotels, transportation facilities, and tourist attractions at different fuzzy neighborhoods. The results indicate that FGWCLQ can reliably discover global prevalent spatial associations among diverse facility types and visualize the spatial heterogeneity at various spatial scales. Compared to the deterministic GWCLQ method, FGWCLQ delivers more stable and robust results across varying spatial scales and generates more continuous association surfaces, which enable clear visualization of hierarchical clustering. Empirical findings provide valuable insights for optimizing the location of star hotels and supporting decision-making in urban planning. The method is available as an open-source Matlab package, providing a practical tool for diverse spatial association investigations. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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24 pages, 2538 KB

Open AccessArticle

A Spatio-Temporal Evolutionary Embedding Approach for Geographic Knowledge Graph Question Answering

by Chunju Zhang, Chaoqun Chu, Kang Zhou, Shu Wang, Yunqiang Zhu, Jianwei Huang, Zhaofu Wu and Fei Gao

ISPRS Int. J. Geo-Inf. 2025, 14(8), 295; https://doi.org/10.3390/ijgi14080295 - 28 Jul 2025

Viewed by 710

Abstract

In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook the dynamic evolution of entities in both time and space, which limits [...] Read more.

In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook the dynamic evolution of entities in both time and space, which limits their effectiveness in downstream reasoning tasks. To address this, we propose a spatio-temporal evolutionary knowledge embedding approach (ST-EKA) that enhances entity representations by modeling their evolution through type-aware encoding, temporal and spatial decay mechanisms, and context aggregation. ST-EKA integrates four core components, including an entity encoder constrained by relational type consistency, a temporal encoder capable of handling both time points and intervals through unified sampling and feedforward encoding, a multi-scale spatial encoder that combines geometric coordinates with semantic attributes, and an evolutionary knowledge encoder that employs attention-based spatio-temporal weighting to capture contextual dynamics. We evaluate ST-EKA on three representative GeoKG datasets—GDELT, ICEWS, and HAD. The results demonstrate that ST-EKA achieves an average improvement of 6.5774% in AUC and 5.0992% in APR on representation learning tasks. In question answering tasks, it yields a maximum average increase of 1.7907% in AUC and 0.5843% in APR. Notably, it exhibits superior performance in chain queries and complex spatio-temporal reasoning, validating its strong robustness, good interpretability, and practical application value. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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25 pages, 3204 KB

Open AccessArticle

Assessing Spatial Digital Twins for Oil and Gas Projects: An Informed Argument Approach Using ISO/IEC 25010 Model

by Sijan Bhandari and Dev Raj Paudyal

ISPRS Int. J. Geo-Inf. 2025, 14(8), 294; https://doi.org/10.3390/ijgi14080294 - 28 Jul 2025

Viewed by 776

Abstract

With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development [...] Read more.

With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development of spatial digital twins to build digital mining models. Existing studies commonly adopt surveys and case studies as their evaluation approach to validate the feasibility of spatial digital twins and related technologies. However, this approach requires high costs and resources. To address this gap, this study explores the feasibility of the informed argument method within the design science framework. A land survey data model (LSDM)-based digital twin prototype for O & G field design, along with 3D spatial datasets located in Lot 2 on RP108045 at petroleum lease 229 under the Department of Resources, Queensland Government, Australia, was selected as a case for this study. The ISO/IEC 25010 model was adopted as a methodology for this study to evaluate the prototype and Digital Twin Victoria (DTV). It encompasses eight metrics, such as functional suitability, performance efficiency, compatibility, usability, security, reliability, maintainability, and portability. The results generated from this study indicate that the prototype encompasses a standard level of all parameters in the ISO/IEC 25010 model. The key significance of the study is its methodological contribution to evaluating the spatial digital twin models through cost-effective means, particularly under circumstances with strict regulatory requirements and low information accessibility. Full article

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28 pages, 10524 KB

Open AccessArticle

Automating Three-Dimensional Cadastral Models of 3D Rights and Buildings Based on the LADM Framework

by Ratri Widyastuti, Deni Suwardhi, Irwan Meilano, Andri Hernandi and Juan Firdaus

ISPRS Int. J. Geo-Inf. 2025, 14(8), 293; https://doi.org/10.3390/ijgi14080293 - 28 Jul 2025

Viewed by 1010

Abstract

Before the development of 3D cadastre, cadastral systems were based on 2D representations, which now require transformation or updating. In this context, the first issue is that existing 2D rights are not aligned with recent 3D data acquired using advanced technologies such as [...] Read more.

Before the development of 3D cadastre, cadastral systems were based on 2D representations, which now require transformation or updating. In this context, the first issue is that existing 2D rights are not aligned with recent 3D data acquired using advanced technologies such as Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR). The second issue is that point clouds of objects captured by UAV-LiDAR, such as fences and exterior building walls—are often neglected. However, these point cloud objects can be utilized to adjust 2D rights to correspond with recent 3D data and to update 3D building models with a higher level of detail. This research leverages such point cloud objects to automatically generate 3D rights and building models. By combining several algorithms, such as Iterative Closest Point (ICP), Random Forest (RF), Gaussian Mixture Model (GMM), Region Growing, the Polyfit method, and the orthogonality concept—an automatic workflow for generating 3D cadastral models is developed. The proposed workflow improves the horizontal accuracy of the updated 2D parcels from 1.19 m to 0.612 m. The floor area of the 3D models improves by approximately ±3 m². Furthermore, the resulting 3D building models provide approximately 43% to 57% of the elements required for 3D property valuation. The case study of this research is in Indonesia. Full article

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22 pages, 573 KB

Open AccessArticle

Towards an Extensible and Text-Oriented Analytical Semantic Trajectory Framework

by Damião Ribeiro de Almeida, Cláudio de Souza Baptista, Fabio Gomes de Andrade and Anselmo Cardoso de Paiva

ISPRS Int. J. Geo-Inf. 2025, 14(8), 292; https://doi.org/10.3390/ijgi14080292 - 28 Jul 2025

Viewed by 516

Abstract

Semantically enriched trajectories have attracted growing interest in recent research, driven by the need for more expressive and context-aware movement data analysis. Two primary approaches have emerged for the storage and management of such data: moving object databases, which operate at the transactional [...] Read more.

Semantically enriched trajectories have attracted growing interest in recent research, driven by the need for more expressive and context-aware movement data analysis. Two primary approaches have emerged for the storage and management of such data: moving object databases, which operate at the transactional or operational level, and trajectory data warehouses (TDWs), which support analytical processing within decision support systems. Conventional TDW methodologies typically model semantic aspects of trajectories by introducing new dimensions into the data warehouse schema. However, this approach often requires structural modifications to the schema in order to accommodate additional semantic attributes, potentially resulting in significant disruptions to the architecture and maintenance of the underlying decision support systems. To overcome this limitation, we propose a novel TDW model that supports dynamic and extensible integration of semantic aspects, without necessitating changes to the schema. This design enhances flexibility and promotes seamless adaptability to domain-specific requirements. To enable such extensibility, we propose an innovative approach to representing semantic trajectories by leveraging natural language processing (NLP) techniques. without relying on traditional spatiotemporal features. This enables the analysis of semantic movement patterns purely through textual context. Finally, we present a comprehensive framework that implements the proposed model in real-world application scenarios, demonstrating its practical extensibility. Full article

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22 pages, 5960 KB

Open AccessArticle

Application of Integrated Geospatial Analysis and Machine Learning in Identifying Factors Affecting Ride-Sharing Before/After the COVID-19 Pandemic

by Afshin Allahyari and Farideddin Peiravian

ISPRS Int. J. Geo-Inf. 2025, 14(8), 291; https://doi.org/10.3390/ijgi14080291 - 28 Jul 2025

Viewed by 643

Abstract

Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after [...] Read more.

Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article

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27 pages, 10737 KB

Open AccessArticle

XT-SECA: An Efficient and Accurate XGBoost–Transformer Model for Urban Functional Zone Classification

by Xin Gao, Xianmin Wang, Li Cao, Haixiang Guo, Wenxue Chen and Xing Zhai

ISPRS Int. J. Geo-Inf. 2025, 14(8), 290; https://doi.org/10.3390/ijgi14080290 - 25 Jul 2025

Viewed by 646

Abstract

The remote sensing classification of urban functional zones provides scientific support for urban planning, land resource optimization, and ecological environment protection. However, urban functional zone classification encounters significant challenges in accuracy and efficiency due to complicated image structures, ambiguous critical features, and high [...] Read more.

The remote sensing classification of urban functional zones provides scientific support for urban planning, land resource optimization, and ecological environment protection. However, urban functional zone classification encounters significant challenges in accuracy and efficiency due to complicated image structures, ambiguous critical features, and high computational complexity. To tackle these challenges, this work proposes a novel XT-SECA algorithm employing a strengthened efficient channel attention mechanism (SECA) to integrate the feature-extraction XGBoost branch and the feature-enhancement Transformer feedforward branch. The SECA optimizes the feature-fusion process through dynamic pooling and adaptive convolution kernel strategies, reducing feature confusion between various functional zones. XT-SECA is characterized by sufficient learning of complex image structures, effective representation of significant features, and efficient computational performance. The Futian, Luohu, and Nanshan districts in Shenzhen City are selected to conduct urban functional zone classification by XT-SECA, and they feature administrative management, technological innovation, and commercial finance functions, respectively. XT-SECA can effectively distinguish diverse functional zones such as residential zones and public management and service zones, which are easily confused by current mainstream algorithms. Compared with the commonly adopted algorithms for urban functional zone classification, including Random Forest (RF), Long Short-Term Memory (LSTM) network, and Multi-Layer Perceptron (MLP), XT-SECA demonstrates significant advantages in terms of overall accuracy, precision, recall, F1-score, and Kappa coefficient, with an accuracy enhancement of 3.78%, 42.86%, and 44.17%, respectively. The Kappa coefficient is increased by 4.53%, 51.28%, and 52.73%, respectively. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)

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ISPRS Int. J. Geo-Inf., Volume 14, Issue 8 (August 2025) – 39 articles

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