ISPRS International Journal of Geo-Information

23 pages, 19328 KiB

Open AccessArticle

TravelRAG: A Tourist Attraction Retrieval Framework Based on Multi-Layer Knowledge Graph

by Sihan Song, Chuncheng Yang, Li Xu, Haibin Shang, Zhuo Li and Yinghui Chang

ISPRS Int. J. Geo-Inf. 2024, 13(11), 414; https://doi.org/10.3390/ijgi13110414 (registering DOI) - 16 Nov 2024

A novel framework called TravelRAG is introduced in this paper, which is built upon a large language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with knowledge graphs to create a retrieval system framework designed for the tourism domain. This framework seeks to address [...] Read more.

A novel framework called TravelRAG is introduced in this paper, which is built upon a large language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with knowledge graphs to create a retrieval system framework designed for the tourism domain. This framework seeks to address the challenges LLMs face in providing precise and contextually appropriate responses to domain-specific queries in the tourism field. TravelRAG extracts information related to tourist attractions from User-Generated Content (UGC) on social media platforms and organizes it into a multi-layer knowledge graph. The travel knowledge graph serves as the core retrieval source for the LLM, enhancing the accuracy of information retrieval and significantly reducing the generation of erroneous or fabricated responses, often termed as “hallucinations”. As a result, the accuracy of the LLM’s output is enhanced. Comparative analyses with traditional RAG pipelines indicate that TravelRAG significantly boosts both the retrieval efficiency and accuracy, while also greatly reducing the computational cost of model fine-tuning. The experimental results show that TravelRAG not only outperforms traditional methods in terms of retrieval accuracy but also better meets user needs for content generation. Full article

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17 pages, 5463 KiB

Open AccessArticle

Geographically-Informed Modeling and Analysis of Platform Attitude Jitter in GF-7 Sub-Meter Stereo Mapping Satellite

by Haoran Xia, Xinming Tang, Fan Mo, Junfeng Xie and Xiang Li

ISPRS Int. J. Geo-Inf. 2024, 13(11), 413; https://doi.org/10.3390/ijgi13110413 (registering DOI) - 15 Nov 2024

Abstract

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are [...] Read more.

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations. Full article

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24 pages, 7975 KiB

Open AccessArticle

Spatial-Temporal Changes in Ecosystem Service Value and Its Overlap with Coal Mining Intensity in the Yellow River Basin, China, During 2000–2030

by Yongjun Yang, Renjie Gong, Qinyu Wu and Fu Chen

ISPRS Int. J. Geo-Inf. 2024, 13(11), 412; https://doi.org/10.3390/ijgi13110412 - 14 Nov 2024

Abstract

Understanding the ecosystem services and their interaction with coal resource development is crucial for formulating sustainable development policies. In this study, we focused on the Yellow River Basin, characterized by both rich coal resources and ecological fragility. The key findings are that (1) [...] Read more.

Understanding the ecosystem services and their interaction with coal resource development is crucial for formulating sustainable development policies. In this study, we focused on the Yellow River Basin, characterized by both rich coal resources and ecological fragility. The key findings are that (1) the ecosystem service value (ESV) in the Yellow River Basin exhibited significant spatial heterogeneity during 2000–2030, decreasing from the southeast to northwest, and decreasing the most notably in the southern part of the upper reaches of the river basin; (2) the high-high clustering area of the ESV shifted from the upper-middle reaches in 2000 to the middle-lower reaches in 2020, while the low-low clustering area remained within Inner Mongolia. By 2030, the high-high clustering area is expected to stabilize in southern Shaanxi Province, and the low-low area will potentially spread eastward; (3) the overall ESV is low, and it experienced a significant decline from 2000 to 2020, with water supply emerging as a major limiting factor, although some policy-supported counties had better ecological service values and trends. (4) From 2000 to 2020, the coal mining intensity (CMI) was concentrated in the upper and middle reaches, particularly at the junctions of Shanxi, Shaanxi, and Inner Mongolia, and the pattern remained stable, but local areas experienced increased mining intensity; (5) the overlap of the CMI and ESV primarily exhibited a low-high clustering pattern in the middle and upper reaches of the Yellow River Basin and eastern Ordos City, and a high-high clustering pattern in the middle reaches of the basin in Shanxi Province, which remained stable and slightly expanded from 2000 to 2030; (6) the trade-off between the ecosystem services in the overlap area intensified, especially between the provisioning and support services, and was significantly impacted by the coal mining activities. The findings indicate that the area that overlaps with the coal mining area in the Yellow River Basin has expanded and has had an increasing negative impact on the ESV. It is also essential to address the trade-offs between the provisioning and support services and to implement ecological restoration measures to mitigate the risk of ESV loss. Future efforts should focus on the regions where the CMI and ESV overlap and have poor coordination and the adverse effects of resource extraction on ecosystem services are becoming more pronounced. The results of this study demonstrate that spatial overlap analysis is effective in identifying the hotspots and provides a foundation for developing sustainable and high-quality policies for ecologically fragile basins. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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17 pages, 17173 KiB

Open AccessArticle

Classifying the Shapes of Buildings by Combining Distance Field Enhancement and a Convolution Neural Network

by Xinyan Zou, Min Yang, Siyu Li and Hai Hu

ISPRS Int. J. Geo-Inf. 2024, 13(11), 411; https://doi.org/10.3390/ijgi13110411 - 14 Nov 2024

Abstract

The shape classification of building objects is crucial in fields such as map generalization and spatial queries. Recently, convolutional neural networks (CNNs) have been used to capture high-level features and classify building shape patterns based on raster representations. However, this raster-based deep learning [...] Read more.

The shape classification of building objects is crucial in fields such as map generalization and spatial queries. Recently, convolutional neural networks (CNNs) have been used to capture high-level features and classify building shape patterns based on raster representations. However, this raster-based deep learning method binarizes the areas into building and non-building zones and does not account for the distance information between these areas, potentially leading to the loss of shape feature information. To address this limitation, this study introduces a building shape classification method that incorporates distance field enhancement with a CNN. In this approach, the distance from various pixels to the building boundary is fused into the image data through distance field enhancement computation. The CNN model, specifically InceptionV3, is then employed to learn and classify building shapes using these enhanced images. The experimental results indicate that the accuracy of building shape classification improved by more than 2.5% following distance field enhancement. Notably, the classification accuracies for F-shaped and T-shaped buildings increased significantly by 4.34% and 11.76%, respectively. Moreover, the proposed method demonstrated a strong performance in classifying other building datasets, suggesting its substantial potential for enhancing shape classification in various applications. Full article

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20 pages, 4625 KiB

Open AccessArticle

Delineations for Police Patrolling on Street Network Segments with p-Median Location Models

by Changho Lee, Hyun Kim, Yongwan Chun and Daniel A. Griffith

ISPRS Int. J. Geo-Inf. 2024, 13(11), 410; https://doi.org/10.3390/ijgi13110410 - 13 Nov 2024

Abstract

Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget [...] Read more.

Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget and human resources for traffic safety, delineating geographic areas optimally for police patrol areas is an important agenda item. This paper considers two p-median location models using segments on a street network as observational units on which traffic issues such as vehicle crashes occur. It also uses two weight sets to construct an enhanced delineation of police patrol areas in the City of Plano, Texas. The first model for the standard p-median formulation gives attention to the cumulative number of motor vehicle crashes from 2011 to 2021 on the major transportation networks in Plano. The second model, an extension of this first p-median one, uses balancing constraints to achieve balanced spatial coverage across patrol areas. These two models are also solved with network kernel density count estimates (NKDCE) instead of crash counts. These smoothed densities on a network enable consideration of uncertainty affiliated with this aggregation. The analysis results of this paper suggest that the p-median models provide effective specifications, including their capability to define patrol areas that encompass the entire study region while minimizing distance costs. The inclusion of balancing constraints ensures a more equitable distribution of workloads among patrol areas, improving overall efficiency. Additionally, the model with NKDCE results in an improved workload balance among delineated areas for police patrolling activities, thus supporting more informed spatial decision-making processes for public safety. Full article

(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)

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26 pages, 9635 KiB

Open AccessArticle

A Raster-Based Multi-Objective Spatial Optimization Framework for Offshore Wind Farm Site-Prospecting

by Loukas Katikas, Themistoklis Kontos, Panayiotis Dimitriadis and Marinos Kavouras

ISPRS Int. J. Geo-Inf. 2024, 13(11), 409; https://doi.org/10.3390/ijgi13110409 - 13 Nov 2024

Abstract

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based [...] Read more.

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea. Full article

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18 pages, 3875 KiB

Open AccessArticle

Spatiotemporal Dynamics of Water Quality: Long-Term Assessment Using Water Quality Indices and GIS

by Dániel Balla, Emőke Kiss, Marianna Zichar and Tamás Mester

ISPRS Int. J. Geo-Inf. 2024, 13(11), 408; https://doi.org/10.3390/ijgi13110408 - 12 Nov 2024

Abstract

The severe contamination of groundwater supplies in rural areas is a global problem that requires strict environmental measures. Related to this, one of the most important challenges at present is the elimination of local sources of pollution. Therefore, this research examined the local [...] Read more.

The severe contamination of groundwater supplies in rural areas is a global problem that requires strict environmental measures. Related to this, one of the most important challenges at present is the elimination of local sources of pollution. Therefore, this research examined the local water quality changes following the construction of the sewerage network, under the framework of long-term monitoring (2011–2022) in Báránd, Hungary, using water quality indices and GIS (Geographic Information System) techniques. In order to understand the purification processes and spatial and temporal changes, three periods were determined: the pre-sewerage period (2011–2014), the transitional period (2015–2018), and the post-sewerage period (2019–2022). Forty monitoring wells were included in the study, ensuring complete coverage of the municipality. The results revealed a high level of pollution in the area in the pre-sewerage period. Based on the calculated indices, an average of 80% of the wells were ranked in categories 4–5, indicating poor water quality, while less than 8% were classified in categories 1–2, indicating good water quality. No significant purification process was detected in the transitional period. However, marked changes were observed in the post-sewerage period as a result of the elimination of local sources of pollution. In the post-sewerage period, the number of monitoring wells ranked as excellent and good increased significantly. Additionally, the number of wells assigned to category 5 decreased markedly, compared to the reference period. The significant difference between the three periods was confirmed by the Wilcoxon test as well (p < 0.05). Based on interpolated maps, it was found that, in the post-sewerage period, an increasing section of the settlement had good or excellent water quality. In addition to an assessment of long-term tendencies, the annual fluctuations in the water quality of the wells were also examined. This showed that the purification processes do not occur in a linear pattern but are influenced by various factors (e.g., precipitation). Our results highlight the importance of protecting and improving groundwater resources in municipal areas and the relevance of long-term monitoring of water adequate management policy. 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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13 pages, 3353 KiB

Open AccessArticle

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

by Yunfei Zhang, Zhengrui Pan, Fangqi Zhu, Chaoyang Shi and Xue Yang

ISPRS Int. J. Geo-Inf. 2024, 13(11), 407; https://doi.org/10.3390/ijgi13110407 - 12 Nov 2024

Abstract

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. [...] Read more.

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks. Full article

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25 pages, 15332 KiB

Open AccessArticle

Identification and Causes of Neighborhood Commercial Areas: Focusing on the Development of Daily Life Circles in Urban Built Environments

by Tianyi Feng and Ying Zhou

ISPRS Int. J. Geo-Inf. 2024, 13(11), 406; https://doi.org/10.3390/ijgi13110406 - 11 Nov 2024

Abstract

Urban planning in China is shifting from an administrative unit-based approach to community life circle planning, aiming to align planning units with residents’ actual activity ranges. As the most fundamental life circle, daily life circle (DLC) planning must adopt a bottom-up approach. However, [...] Read more.

Urban planning in China is shifting from an administrative unit-based approach to community life circle planning, aiming to align planning units with residents’ actual activity ranges. As the most fundamental life circle, daily life circle (DLC) planning must adopt a bottom-up approach. However, the widely applicable methods for delineating DLCs remain lacking. This study presents a strategy for delineating DLCs centered on neighborhood commercial areas that aggregate essential daily life services. Correspondingly, a method is proposed for identifying neighborhood commercial areas based on residents’ actual usage of facilities. The method was applied in Qinhuai District, Nanjing, where neighborhood commercial areas were identified and the factors influencing their formation and types were quantitatively analyzed. The results indicate the following: (1) the proposed method accurately identifies neighborhood commercial areas that can serve as DLC central areas; (2) commercial diversity, public transportation stops, and parking spots are the three most influential factors in neighborhood commercial area formation, exhibiting non-linear and threshold effects; and (3) the type of neighborhood commercial areas varies by population density, housing prices, and street betweenness, with betweenness being the most significant factor. These findings provide methods and indicators for DLC delineation and planning, contributing to the realization of the DLC construction concept. Full article

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20 pages, 575 KiB

Open AccessArticle

Large Language Model-Driven Structured Output: A Comprehensive Benchmark and Spatial Data Generation Framework

by Diya Li, Yue Zhao, Zhifang Wang, Calvin Jung and Zhe Zhang

ISPRS Int. J. Geo-Inf. 2024, 13(11), 405; https://doi.org/10.3390/ijgi13110405 - 10 Nov 2024

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a [...] Read more.

Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generation. We present a multi-step workflow designed to improve the accuracy and efficiency of spatial data generation. The steps include generating spatial data (e.g., GeoJSON) and implementing a novel method for indexing R-tree structures. In addition, we explore and compare a series of methods commonly used by developers and researchers to enable LLMs to produce structured outputs, including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG). We propose new metrics and datasets along with a new method for evaluating the quality and consistency of these outputs. Our findings offer valuable insights into the strengths and limitations of each approach, guiding practitioners in selecting the most suitable method for their specific use cases. This work advances the field of LLM-based structured spatial data output generation and supports the seamless integration of LLMs into real-world applications. Full article

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

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17 pages, 2157 KiB

Open AccessArticle

Analysis of Decoupling Effects and Influence Factors in Transportation: Evidence from Guangdong Province, China

by Hualing Bi, Shiying Zhang and Fuqiang Lu

ISPRS Int. J. Geo-Inf. 2024, 13(11), 404; https://doi.org/10.3390/ijgi13110404 - 8 Nov 2024

Abstract

In recent years, global environmental issues have become increasingly prominent. The transportation industry, as the fundamental sector of national economic development, is also characterized by high energy consumption and carbon emissions. Therefore, it is imperative to conduct research on the carbon emission problem [...] Read more.

In recent years, global environmental issues have become increasingly prominent. The transportation industry, as the fundamental sector of national economic development, is also characterized by high energy consumption and carbon emissions. Therefore, it is imperative to conduct research on the carbon emission problem within this industry. In light of the Tapio decoupling model, an analysis of the correlation between traffic carbon emissions and economic development in Guangdong province during 1999–2019 was carried out. With the aim of encouraging Guangdong province’s low-carbon transportation development, the factors affecting the transportation industry are analyzed utilizing the generalized Divisia index model (GDIM). We also introduced passenger and freight turnover as an influencing factor for analysis. The findings indicate that (1) Guangdong province’s traffic carbon emissions increased from 1999 to 2019; (2) the traffic carbon emissions’ decoupling effect is mainly “weakly decoupled”, and the overall decoupling effect is not strong in Guangdong province; (3) among the traffic carbon emissions’ factors, the effects of the production value of traffic and the turnover volume are at the forefront, and the effect of turnover volume has gradually exceeded the production value of traffic in recent years. The suppression of the intensity of carbon emissions is relatively large, while the suppression of the intensity of energy consumption and transport is relatively weak. Based on this, strategies were proposed to promote a cleaner energy mix, improve energy use efficiency, create energy savings, develop green technologies, and foster the restructuring of transportation. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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23 pages, 6550 KiB

Open AccessArticle

Examining Spatial Accessibility and Equity of Public Hospitals for Older Adults in Songjiang District, Shanghai

by Mirkamiljan Mahmut, Pei Yin, Bozhezi Peng, Jiani Wu, Tao Wang, Shengqiang Yuan and Yi Zhang

ISPRS Int. J. Geo-Inf. 2024, 13(11), 403; https://doi.org/10.3390/ijgi13110403 - 7 Nov 2024

Abstract

In developing countries, aging is rapid and new towns in suburban and rural districts are emerging. However, the spatial accessibility and equity of healthcare services for older adults in new towns is rarely examined. This study is among the earliest attempts to evaluate [...] Read more.

In developing countries, aging is rapid and new towns in suburban and rural districts are emerging. However, the spatial accessibility and equity of healthcare services for older adults in new towns is rarely examined. This study is among the earliest attempts to evaluate the spatial accessibility and equity of public hospitals for older adults, using data from Songjiang District, Shanghai, China. A modified Gaussian Huff-based three-step floating catchment area (GH3SFCA) method was adopted based on the real-time travel costs of public transit, driving, cycling, and walking. The Gini coefficient and Bivariate Moran’s Index were integrated to estimate spatial equity. The results showed that the spatial accessibility of high-tier hospitals decreases from the central areas to the outskirts for older adults in Songjiang. Meanwhile, the accessibility of low-tier hospitals varies substantially across areas. Although the low-tier hospitals are distributed evenly, their Gini coefficient showed less equitable spatial accessibility than the high-tier hospitals. Furthermore, driving and cycling lead to more equitable spatial accessibility than public transit or walking. Finally, communities with a low-supply–high-demand mismatch for public hospitals were suggested to be improved preferentially. These findings will facilitate planning strategies for public hospitals for older adults in developing new towns. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)

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20 pages, 3724 KiB

Open AccessArticle

Unveiling Urban River Visual Features Through Immersive Virtual Reality: Analyzing Youth Perceptions with UAV Panoramic Imagery

by Yunlei Shou, Zexin Lei, Jiaying Li and Junjie Luo

ISPRS Int. J. Geo-Inf. 2024, 13(11), 402; https://doi.org/10.3390/ijgi13110402 - 7 Nov 2024

Abstract

The visual evaluation and characteristic analysis of urban rivers are pivotal for advancing our understanding of urban waterscapes and their surrounding environments. Unmanned aerial vehicles (UAVs) offer significant advantages over traditional satellite remote sensing, including flexible aerial surveying, diverse perspectives, and high-resolution imagery. [...] Read more.

The visual evaluation and characteristic analysis of urban rivers are pivotal for advancing our understanding of urban waterscapes and their surrounding environments. Unmanned aerial vehicles (UAVs) offer significant advantages over traditional satellite remote sensing, including flexible aerial surveying, diverse perspectives, and high-resolution imagery. This study centers on the Haihe River, South Canal, and North Canal in Tianjin China, employing UAVs to capture continuous panoramic image data. Through immersive virtual reality (VR) technology, visual evaluations of these panoramic images were obtained from a cohort of young participants. These evaluations encompassed assessments of scenic beauty, color richness, vitality, and historical sense. Subsequently, computer vision techniques were utilized to quantitatively analyze the proportions of various landscape elements (e.g., trees, grass, buildings) within the images. Clustering analysis of visual evaluation results and semantic segmentation outcomes from different study points facilitated the effective identification and grouping of river visual features. The findings reveal significant differences in scenic beauty, color richness, and vitality among the Haihe River, South Canal, and North Canal, whereas the South and North Canals exhibited a limited sense of history. Six landscape elements—water bodies, buildings, trees, etc.—comprised over 90% of the images, forming the primary visual characteristics of the three rivers. Nonetheless, the uneven spatial distribution of these elements resulted in notable variations in the visual features of the rivers. This study demonstrates that the visual feature analysis method based on UAV panoramic images can achieve a quantitative evaluation of multi-scene urban 3D landscapes, thereby providing a robust scientific foundation for the optimization of urban river environments. Full article

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17 pages, 2894 KiB

Open AccessArticle

Open Data for Transparency of Government Tenders: A State Analysis in Croatian Agriculture Land Lease

by Larisa Hrustek, Karlo Kević and Filip Varga

ISPRS Int. J. Geo-Inf. 2024, 13(11), 401; https://doi.org/10.3390/ijgi13110401 - 7 Nov 2024

Abstract

State-owned agricultural land is an asset that the state must manage in a responsible and transparent manner. Agricultural land is extremely important for farmers as it enables them to carry out agricultural activities. Due to its importance to farmers, it is often the [...] Read more.

State-owned agricultural land is an asset that the state must manage in a responsible and transparent manner. Agricultural land is extremely important for farmers as it enables them to carry out agricultural activities. Due to its importance to farmers, it is often the subject of debate as stakeholders are often dissatisfied with the allocation and management of state-owned agricultural land. Qualitative research of the process of state agricultural land lease and the associated legislation in the Republic of Croatia enabled the analysis of the existing business model, with the results pointing to shortcomings in the Initial and Evaluation phases of the process. A steady rise in the number of tenders published in 2015–2022 was recorded. Local administrative units in the Continental region scored higher than those in the Adriatic region (both cities and municipalities) in terms of transparency. Unfortunately, the response rate from the local authorities was below 50% across both region and unit, further indicating low transparency. Based on the findings, a proposal of changes in the tendering process was made utilizing a digital platform as an environment for all stakeholders, which provides functionalities essential for the transparent implementation of tenders for the agricultural land lease in Croatia. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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14 pages, 6699 KiB

Open AccessArticle

TPTrans: Vessel Trajectory Prediction Model Based on Transformer Using AIS Data

by Wentao Wang, Wei Xiong, Xue Ouyang and Luo Chen

ISPRS Int. J. Geo-Inf. 2024, 13(11), 400; https://doi.org/10.3390/ijgi13110400 - 7 Nov 2024

Abstract

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to [...] Read more.

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least

6 \times 10^{- 4}

compared with the baseline model. Full article

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22 pages, 9430 KiB

Open AccessArticle

Using Space Syntax and GIS to Determine Future Growth Routes of Cities: The Case of the Kyrenia White Zone

by Cem Doğu and Cemil Atakara

ISPRS Int. J. Geo-Inf. 2024, 13(11), 399; https://doi.org/10.3390/ijgi13110399 - 7 Nov 2024

Abstract

Cities are in constant development, both structurally and demographically, necessitating careful planning to enhance their orderliness and livability. This research focuses on identifying development directions and routes for the Kyrenia White Zone, situated between the sea and the mountains in northern Cyprus, a [...] Read more.

Cities are in constant development, both structurally and demographically, necessitating careful planning to enhance their orderliness and livability. This research focuses on identifying development directions and routes for the Kyrenia White Zone, situated between the sea and the mountains in northern Cyprus, a significant tourist area. The rapid implementation of zoning laws over different periods has led to swift development and population growth, resulting in various infrastructure challenges, particularly related to transportation. The primary aim of this study is to assess the current infrastructure issues within the zone, understand user perceptions, and identify key factors influencing future growth. Based on the collected data, we propose an alternative growth area for the future development plan of the city. Additionally, this research seeks to explore irregular urban developments and make informed design decisions for their future. Utilizing Space Syntax and GIS as core methodologies, the study employs Space Syntax, a research method developed by Bill Hillier and Julienne Hanson in the 1970s, to analyze human movement and perception. The existing map system of the Kyrenia White Zone was digitized, and essential geographical information was gathered. This data were analyzed using GIS and evaluated through the Space Syntax method. The analysis yielded alternative growth routes that address current challenges within the zone, accompanied by recommendations for enhancing its future development. Full article

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20 pages, 11747 KiB

Open AccessArticle

An Improved Generative Adversarial Network for Generating Multi-Scale Electronic Map Tiles Considering Cartographic Requirements

by Wei Zhu, Qingsheng Guo, Nai Yang, Ying Tong and Chuanbang Zheng

ISPRS Int. J. Geo-Inf. 2024, 13(11), 398; https://doi.org/10.3390/ijgi13110398 - 7 Nov 2024

Abstract

Multi-scale electronic map tiles are important basic geographic information data, and an approach based on deep learning is being used to generate multi-scale map tiles. Although generative adversarial networks (GANs) have demonstrated great potential in single-scale electronic map tile generation, further research concerning [...] Read more.

Multi-scale electronic map tiles are important basic geographic information data, and an approach based on deep learning is being used to generate multi-scale map tiles. Although generative adversarial networks (GANs) have demonstrated great potential in single-scale electronic map tile generation, further research concerning multi-scale electronic map tile generation is needed to meet cartographic requirements. We designed a multi-scale electronic map tile generative adversarial network (MsM-GAN), which consisted of several GANs and could generate map tiles at different map scales sequentially. Road network data and building footprint data from OSM (Open Street Map) were used as auxiliary information to provide the MsM-GAN with cartographic knowledge about spatial shapes and spatial relationships when generating electronic map tiles from remote sensing images. The map objects which should be deleted or retained at the next map scale according to cartographic standards are encoded as auxiliary information in the MsM-GAN when generating electronic map tiles at smaller map scales. In addition, in order to ensure the consistency of the features learned by several GANs, the density maps constructed from specific map objects are used as global conditions in the MsM-GAN. A multi-scale map tile dataset was collected from MapWorld, and experiments on this dataset were conducted using the MsM-GAN. The results showed that compared to other image-to-image translation models (Pix2Pix and CycleGAN), the MsM-GAN shows average increases of 10.47% in PSNR and 9.92% in SSIM and has the minimum MSE values at all four map scales. The MsM-GAN also performs better in visual evaluation. In addition, several comparative experiments were completed to verify the effect of the proposed improvements. Full article

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15 pages, 2134 KiB

Open AccessArticle

Space–Time Analysis of the COVID-19 Pandemic and Its Relationship with Socioeconomic and Demographic Variables in the Metropolitan Region of São Paulo, Brazil

by Keila Valente de Souza de Santana, Aluízio Marino, Gabriela Rosa Martins, Pedro Henrique Barbosa Muniz Lima, Pedro Henrique Rezende Mendonça and Raquel Rolnik

ISPRS Int. J. Geo-Inf. 2024, 13(11), 397; https://doi.org/10.3390/ijgi13110397 - 7 Nov 2024

Abstract

This study sought to identify clusters of a high and low risk of incidence and mortality from COVID-19 throughout the pandemic period, from 2020 to 2022, in the Metropolitan Region of São Paulo (MRSP), analyzing their relationship with socioeconomic and demographic variables. Spatiotemporal [...] Read more.

This study sought to identify clusters of a high and low risk of incidence and mortality from COVID-19 throughout the pandemic period, from 2020 to 2022, in the Metropolitan Region of São Paulo (MRSP), analyzing their relationship with socioeconomic and demographic variables. Spatiotemporal and temporal variations in the clusters were determined using scan statistics, a multidimensional point process that performs multiple tests for each geographic point analyzed, in SaTScan v10.0. Socioeconomic and demographic differences were analyzed using the nonparametric Mann–Whitney and Kruskal–Wallis tests. Temporal clusters of high incidence and high mortality were observed in May 2020 and March to June 2021. In the spatiotemporal analysis, the clusters of high incidence and high mortality were concentrated in the city of São Paulo and neighboring cities, indicating that the capital was an area of influence and convergence at all times during the COVID-19 pandemic. Clusters of low mortality were found in the central region of the capital, which concentrates the highest incomes and the lowest percentages of Black, mixed-race, and Indigenous people in the MRSP. All clusters were identified in densely occupied areas and point to a pattern of disease spread that is related to income and ethnicity, as well as to the circulation dynamics of a metropolitan region. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)

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19 pages, 4363 KiB

Open AccessArticle

Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing

by Le Chen, Simin Yu, Shi Shen, You Wan and Changqing Song

ISPRS Int. J. Geo-Inf. 2024, 13(11), 396; https://doi.org/10.3390/ijgi13110396 - 6 Nov 2024

Abstract

The carbon sequestration capacity of vegetation is the key to the carbon cycle in terrestrial ecosystems. It is significant to analyze the spatiotemporal variation and influencing factors of vegetation carbon sequestration ability to improve territorial carbon sink and optimize its spatial pattern. However, [...] Read more.

The carbon sequestration capacity of vegetation is the key to the carbon cycle in terrestrial ecosystems. It is significant to analyze the spatiotemporal variation and influencing factors of vegetation carbon sequestration ability to improve territorial carbon sink and optimize its spatial pattern. However, there is a lack of understanding of the impact of environmental conditions and human activity on the vegetation’s carbon sequestration ability, especially in highly urbanized areas. For example, effective vegetation management methods can enhance vegetation Gross Primary Productivity, while emissions of air pollutants like O₃, CO, NO₂, and PM2.5 can suppress it. This paper mainly explores the factors influencing vegetation carbon sequestration capacity across different regions of Beijing. Based on remote sensing data and site observation data, this paper analyzed the spatiotemporal variation trend of Annual Gross Primary Production (AGPP) and the influence of environmental factors and human activity factors on GPP in Beijing from 2000 to 2020 by using the Theil−Sen’s slope estimator, Mann−Kendall trend test, and comparing Geographically Weighted Regression method (GWR) and Geographically and Temporally Weighted Regression method (GTWR). GWR is a localized multiple regression technique used to estimate variable relationships that vary spatially. GTWR extends GWR by adding temporal analysis, enabling a comprehensive examination of spatiotemporal data variations. Besides, we used land use cover data to discuss the influence of land use cover change on AGPP. The results showed that the spatial distribution pattern of GPP in Beijing was higher in the northwest and lower in the southeast, and it showed an overall upward trend from 2000 to 2020, with an average annual growth rate of 14.39 g C·m⁻²·a⁻¹. From 2000 to 2020, excluding the core urban areas, the GPP of 95.8% of Beijing increased, and 10.6% of Beijing showed a trend of significant increase, concentrated in Mentougou, Changping, and Miyun. GPP decreased in 4.1% of the regions in Beijing and decreased significantly in 1.4% of the areas within the sixth ring. The areas where AGPP significantly decreased were concentrated in those where land use types were converted to Residential land (impervious land), while AGPP showed an upward trend in other areas. CO and NO₂ are the main driving forces of GPP change in Beijing. O₃ and land surface temperature (LST) also exert certain influences, while the impact of precipitation (PRE) is relatively minor. O₃ and CO have a positive impact on AGPP as a whole, while LST and NO₂ generally exhibit negative impacts. PRE has a positive impact in the central area of Beijing, while it has a negative impact in the peripheral areas. This study further discusses opinions on future urbanization and environmental management policies in Beijing, which will promote the carbon peak and carbon neutrality process of ecological space management in Beijing. Besides, this study was conducted at the urban scale rather than at ecological sites, encompassing a variety of factors that influence vegetation AGPP. Consequently, the results also offer fresh insights into the intricate nexus between human activities, pollutants, and the GPP of vegetation. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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