ISPRS International Journal of Geo-Information

14 pages, 8636 KiB

Open AccessArticle

Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction

by Hasan Kemik, Tugba Dalyan and Murat Aydogan

ISPRS Int. J. Geo-Inf. 2024, 13(12), 449; https://doi.org/10.3390/ijgi13120449 - 13 Dec 2024

Finding a parking space is a major concern in megacities, causing drivers to lose time and money while contributing to atmospheric pollution and global warming. This study proposes a method to predict parking slot availability by comparing Long Short-Term Memory (LSTM) and Multi-Head [...] Read more.

Finding a parking space is a major concern in megacities, causing drivers to lose time and money while contributing to atmospheric pollution and global warming. This study proposes a method to predict parking slot availability by comparing Long Short-Term Memory (LSTM) and Multi-Head Attention (MHA) methods using the CityPulse Smart City Datasets. The initial experiments assessed the impact of pollution and time features on prediction accuracy. In a subsequent experiment, the dataset was expanded by incorporating weather-related features and a broader time range while excluding pollution and time features, as informed by the initial results. Various experiments were conducted with different parameters, such as model depth and activation functions. The results demonstrated that MHA outperformed LSTM in predicting occupancy rates, achieving a Mean Absolute Error (MAE) score of 0.0589 on the extended dataset. This study marks a pioneering effort in using MHA for real-time parking occupancy prediction, showcasing significant success with fewer parameters and a smaller model size. Full article

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18 pages, 6401 KiB

Open AccessArticle

Continuous Satellite Image Generation from Standard Layer Maps Using Conditional Generative Adversarial Networks

by Arminas Šidlauskas, Andrius Kriščiūnas and Dalia Čalnerytė

ISPRS Int. J. Geo-Inf. 2024, 13(12), 448; https://doi.org/10.3390/ijgi13120448 - 11 Dec 2024

Abstract

Satellite image generation has a wide range of applications. For example, parts of images must be restored in areas obscured by clouds or cloud shadows or areas that must be anonymized. The need to cover a large area with the generated images faces [...] Read more.

Satellite image generation has a wide range of applications. For example, parts of images must be restored in areas obscured by clouds or cloud shadows or areas that must be anonymized. The need to cover a large area with the generated images faces the challenge that separately generated images must maintain the structural and color continuity between the adjacent generated images as well as the actual ones. This study presents a modified architecture of the generative adversarial network (GAN) pix2pix that ensures the integrity of the generated remote sensing images. The pix2pix model comprises a U-Net generator and a PatchGAN discriminator. The generator was modified by expanding the input set with images representing the known parts of ground truth and the respective mask. Data used for the generative model consist of Sentinel-2 (S2) RGB satellite imagery as the target data and OpenStreetMap mapping data as the input. Since forested areas and fields dominate in images, a Kneedle clusterization method was applied to create datasets that better represent the other classes, such as buildings and roads. The original and updated models were trained on different datasets and their results were evaluated using gradient magnitude (GM), Fréchet inception distance (FID), structural similarity index measure (SSIM), and multiscale structural similarity index measure (MS-SSIM) metrics. The models with the updated architecture show improvement in gradient magnitude, SSIM, and MS-SSIM values for all datasets. The average GMs of the junction region and the full image are similar (do not exceed 7%) for the images generated using the modified architecture whereas it is more than 13% higher in the junction area for the images generated using the original architecture. The importance of class balancing is demonstrated by the fact that, for both architectures, models trained on the dataset with a higher ratio of classes representing buildings and roads compared to the models trained on the dataset without clusterization have more than 10% lower FID (162.673 to 190.036 for pix2pix and 173.408 to 195.621 for the modified architecture) and more than 5% higher SSIM (0.3532 to 0.3284 for pix2pix and 0.3575 to 0.3345 for the modified architecture) and MS-SSIM (0.3532 to 0.3284 for pix2pix and 0.3575 to 0.3345 for the modified architecture) values. Full article

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26 pages, 22371 KiB

Open AccessArticle

Generic Method for Social–Environmental System Boundary Delineation—An Amalgamation of Spatial Data Integration, Optimization, and User Control for Resource Management

by Mohammad Shahriyar Parvez and Xin Feng

ISPRS Int. J. Geo-Inf. 2024, 13(12), 447; https://doi.org/10.3390/ijgi13120447 - 10 Dec 2024

Abstract

The Social–Environmental System (SES) framework is crucial in understanding the intricate interplay between human societies and their environmental contexts. Despite its significance, existing SES delineation methods often rely on subjective judgment and struggle with the non-linear, multi-scale nature of SES data, leading to [...] Read more.

The Social–Environmental System (SES) framework is crucial in understanding the intricate interplay between human societies and their environmental contexts. Despite its significance, existing SES delineation methods often rely on subjective judgment and struggle with the non-linear, multi-scale nature of SES data, leading to challenges in effective resource management and policymaking. This research addresses these gaps by proposing a novel, reproducible framework for SES boundary delineation that integrates both vector and raster data, utilizing advanced spatial optimization techniques and dimension reduction algorithms like UMAP to manage the non-linear characteristics of SES. The framework also leverages the SKATER algorithm for precise regionalization, ensuring spatial continuity and compactness while allowing user control over region selection and data dimensions. Applied to the Rio Grande/Bravo Basin, this approach demonstrates the practical utility and computational efficiency of the proposed method, offering a scalable solution adaptable to various regions. While focusing on this transboundary area, the study underscores how its framework can be generalized globally for addressing socio-environmental challenges while maintaining flexibility to accommodate local and regional specificities. The framework’s reliance on open-source tools further enhances its accessibility and reproducibility, making it a valuable contribution to SES research and practical environmental management. Full article

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18 pages, 1467 KiB

Open AccessArticle

Exploring the Activity-Travel Patterns of Multi-Purpose Commuters on Workdays Based on Activity Chains and Time Allocation: Evidence from Kunming, China

by Mingwei He, Na Chen, Yueren He, Jianbo Li and Yang Liu

ISPRS Int. J. Geo-Inf. 2024, 13(12), 446; https://doi.org/10.3390/ijgi13120446 - 10 Dec 2024

Abstract

Understanding activity-travel patterns and their determinants with regard to multi-purpose commuters is essential for enhancing commuting efficiency and ensuring equal participation in activities. This study applies sequence analysis and hierarchical clustering to identify distinct activity-travel patterns of Kunming commuters using 2016 Household Travel [...] Read more.

Understanding activity-travel patterns and their determinants with regard to multi-purpose commuters is essential for enhancing commuting efficiency and ensuring equal participation in activities. This study applies sequence analysis and hierarchical clustering to identify distinct activity-travel patterns of Kunming commuters using 2016 Household Travel Survey data. Subsequently, a multinomial logistic regression model (MNL) examines the factors influencing these patterns. The results reveal significant heterogeneity across four activity-travel patterns: the fixed commuter pattern (FCP), characterized by pronounced morning and evening peaks with minimal non-commuting activities; the balanced commuter pattern (BCP), where commuters participate in non-commuting activities after afternoon work; the restricted commuter pattern (RCP), with non-commuting activities occurring after midday work; and the flexible commuter pattern (FLCP), featuring a late-start work pattern where some commuters go to work after 5 pm. Additionally, the study finds that female commuters and those with longer commuting and working hours tend to have simpler time allocation. Conversely, male commuters, those from complex family structures, car-owning households, and residents in areas with abundant activity opportunities actively engage in non-commuting activities. These findings can help policymakers optimize travel services and develop heterogeneous commuting and transportation policies. Full article

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28 pages, 6709 KiB

Open AccessArticle

A 3D Model-Based Framework for Real-Time Emergency Evacuation Using GIS and IoT Devices

by Noopur Tyagi, Jaiteg Singh, Saravjeet Singh and Sukhjit Singh Sehra

ISPRS Int. J. Geo-Inf. 2024, 13(12), 445; https://doi.org/10.3390/ijgi13120445 - 9 Dec 2024

Abstract

Advancements in 3D modelling technology have facilitated more immersive and efficient solutions in spatial planning and user-centred design. In healthcare systems, 3D modelling is beneficial in various applications, such as emergency evacuation, pathfinding, and localization. These models support the fast and efficient planning [...] Read more.

Advancements in 3D modelling technology have facilitated more immersive and efficient solutions in spatial planning and user-centred design. In healthcare systems, 3D modelling is beneficial in various applications, such as emergency evacuation, pathfinding, and localization. These models support the fast and efficient planning of evacuation routes, ensuring the safety of patients, staff, and visitors, and guiding them in cases of emergency. To improve urban modelling and planning, 3D representation and analysis are used. Considering the advantages of 3D modelling, this study proposes a framework for 3D indoor navigation and employs a multiphase methodology to enhance spatial planning and user experience. Our approach combines state-of-the art GIS technology with a 3D hybrid model. The proposed framework incorporates federated learning (FL) along with edge computing and Internet of Things (IoT) devices to achieve accurate floor-level localization and navigation. In the first phase of the methodology, Quantum Geographic Information System (QGIS) software was used to create a 3D model of the building’s architectural details, which are required for efficient indoor navigation during emergency evacuations in healthcare systems. In the second phase, the 3D model and an FL-based recurrent neural network (RNN) technique were utilized to achieve real-time indoor positioning. This method resulted in highly precise outcomes, attaining an accuracy rate over 99% at distances of no less than 10 metres. Continuous monitoring and effective pathfinding ensure that users can navigate safely and effectively during emergencies. IoT devices were connected with the building’s navigation software in Phase 3. As per the performed analysis, it was observed that the proposed framework provided 98.7% routing accuracy between different locations during emergency situations. By improving safety, building accessibility, and energy efficiency, this research addresses the health and environmental impacts of modern technologies. Full article

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

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18 pages, 2671 KiB

Open AccessArticle

Spatial Pattern and Influencing Factors of Tourist Attractions in Coastal Cities: A Case Study of Qingdao

by Yue Xu, Xuliang Zhang, Kuncheng Zhang, Jing Yu and Jia Liu

ISPRS Int. J. Geo-Inf. 2024, 13(12), 444; https://doi.org/10.3390/ijgi13120444 - 9 Dec 2024

Abstract

The spatial distribution of tourist attractions plays a critical role in the development of coastal cities. Qingdao, with its coastal geography, rich cultural heritage, and rapid urbanization, serves as a representative case. This study integrates POI and multi-source data, employing methods such as [...] Read more.

The spatial distribution of tourist attractions plays a critical role in the development of coastal cities. Qingdao, with its coastal geography, rich cultural heritage, and rapid urbanization, serves as a representative case. This study integrates POI and multi-source data, employing methods such as the average nearest neighbor index, kernel density estimation, standard deviational ellipse, and Geodetector to analyze the spatial characteristics and influencing factors of Qingdao’s tourist attractions. Additionally, path dependence theory is innovatively applied to elucidate the mechanisms of the city’s development trajectory. Both natural and social factors influence this distribution, where the resource environment forms the foundational basis, the economic development provides impetus, and the urban development orientation exerts a regulatory effect. The findings are broadly applicable to other coastal tourist cities and offer strategic insights for sustainable development in such contexts. Full article

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21 pages, 1278 KiB

Open AccessArticle

A Multi-Level Analysis of Bus Ridership in Buffalo, New York

by Chihuangji Wang and Jiyoung Park

ISPRS Int. J. Geo-Inf. 2024, 13(12), 443; https://doi.org/10.3390/ijgi13120443 - 8 Dec 2024

Abstract

It is essential to understand how the built environment affects transit ridership to prioritize public transit and make it more appealing, particularly in mid-sized cities on the Rust Belt due to the experience of population decrease and urban sprawl in the U.S. Although [...] Read more.

It is essential to understand how the built environment affects transit ridership to prioritize public transit and make it more appealing, particularly in mid-sized cities on the Rust Belt due to the experience of population decrease and urban sprawl in the U.S. Although many studies have looked at factors that influence ridership, there is still a need for a methodological design that considers both route and environment characteristics for bus ridership. This study examined the daily ridership of 3794 bus stops across 57 routes in the Buffalo area of New York State and used random coefficients models to account for different levels of characteristics (bus stop level, route level, and transportation analysis zone (TAZ) level). The study found that bus frequency and bus stop centrality were positively correlated with ridership, while total route stops had a negative effect. By controlling the impact of bus routes, the study showed that the multi-level design using random coefficients models was more effective than traditional OLS and spatial lag models in quantifying the impact of bus routes and TAZs. These findings provide local policy implications for route design, bus operation, and transit resource allocation. Full article

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22 pages, 10019 KiB

Open AccessArticle

Entity-Driven New Paradigm of Mine Data: Model Construction and Application

by Wenjing Li, Qian Ma, Yanbin Tang and Zhiyong Lin

ISPRS Int. J. Geo-Inf. 2024, 13(12), 442; https://doi.org/10.3390/ijgi13120442 - 8 Dec 2024

Abstract

In the construction of a three-dimensional real scene (3D real scene) of a mine, we encounter challenges in organizing and correlating multi-source data. To surmount these challenges, we innovatively adopted the entity organization method. We proposed a definition of a “mining entity” and [...] Read more.

In the construction of a three-dimensional real scene (3D real scene) of a mine, we encounter challenges in organizing and correlating multi-source data. To surmount these challenges, we innovatively adopted the entity organization method. We proposed a definition of a “mining entity” and conducted an in-depth analysis of its main characteristics. Based on these characteristics, we classified and coded the main mining entities and further constructed an entity expression pattern layer that could fully reflect the characteristics of the mine. Based on multi-source heterogeneous mine data, we constructed a mining entity data layer. Furthermore, by adopting a graph database approach, we were able to create a graphical representation and correlation application of the complex relationships among mining entities. This work shows that compared to traditional data management methods, a mine data organization approach centered on entities can more effectively integrate and correlate multi-source mine data, providing strong support for the digital management of mines and the construction of 3D real scenes. Full article

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31 pages, 10335 KiB

Open AccessArticle

Real-Time Co-Editing of Geographic Features

by Hrvoje Matijević, Saša Vranić, Nikola Kranjčić and Vlado Cetl

ISPRS Int. J. Geo-Inf. 2024, 13(12), 441; https://doi.org/10.3390/ijgi13120441 - 7 Dec 2024

Abstract

Real-time GIS enables multiple geographically dislocated users to collaboratively edit geospatial data. However, being based on the strong consistency model, traditional real-time GIS implementations cannot provide fully automatic conflict resolution. In highly dynamic situations with increased probability for conflicts, this will hinder user [...] Read more.

Real-time GIS enables multiple geographically dislocated users to collaboratively edit geospatial data. However, being based on the strong consistency model, traditional real-time GIS implementations cannot provide fully automatic conflict resolution. In highly dynamic situations with increased probability for conflicts, this will hinder user experience. Conflict-free replicated data types (CRDTs), a technology based on a more relaxed concurrency control model called strong eventual consistency, can resolve all conflicts in real time, letting the users work on their local copies of the data without any restrictions. The application of CRDTs to real-time geospatial geometry co-editing has, to the best of our knowledge, not been investigated. Within this research, we therefore developed a simple web-based real-time geospatial geometry co-editing system using an existing CRDT implementation in Javascript coupled with OpenLayers. When applied to the co-editing of geospatial geometry in its native form, standard CRDT conflict resolution mechanics exhibit some issues. As an attempt to address these issues, we developed an advanced operation generation technique named “tentative operations”. This technique allows for the operations to be generated over the most recent session-wide state of the data, which in effect highly reduces concurrency and provides “geometry aware” conflict resolution. The tests we conducted using the developed system showed that in low-latency network conditions, the negative effects of standard CRDT conflict resolution mechanics do get minimized even under increased system loads. Full article

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18 pages, 8026 KiB

Open AccessArticle

Estimation of Non-Photosynthetic Vegetation Cover Using the NDVI–DFI Model in a Typical Dry–Hot Valley, Southwest China

by Caiyi Fan, Guokun Chen, Ronghua Zhong, Yan Huang, Qiyan Duan and Ying Wang

ISPRS Int. J. Geo-Inf. 2024, 13(12), 440; https://doi.org/10.3390/ijgi13120440 - 7 Dec 2024

Abstract

Non-photosynthetic vegetation (NPV) significantly impacts ecosystem degradation, drought, and wildfire risk due to its flammable and persistent litter. Yet, the accurate estimation of NPV in heterogeneous landscapes, such as dry–hot valleys, has been limited. This study utilized multi-source time-series remote sensing data from [...] Read more.

Non-photosynthetic vegetation (NPV) significantly impacts ecosystem degradation, drought, and wildfire risk due to its flammable and persistent litter. Yet, the accurate estimation of NPV in heterogeneous landscapes, such as dry–hot valleys, has been limited. This study utilized multi-source time-series remote sensing data from Sentinel-2 and GF-2, along with field surveys, to develop an NDVI-DFI ternary linear mixed model for quantifying NPV coverage (f_NPV) in a typical dry–hot valley region in 2023. The results indicated the following: (1) The NDVI-DFI ternary linear mixed model effectively estimates photosynthetic vegetation coverage (f_PV) and f_NPV, aligning well with the conceptual framework and meeting key assumptions, demonstrating its applicability and reliability. (2) The RGB color composite image derived using the minimum inclusion endmember feature method (MVE) exhibited darker tones, suggesting that MVE tends to overestimate the vegetation fraction when distinguishing vegetation types from bare soil. On the other hand, the pure pixel index (PPI) method showed higher accuracy in estimation due to its higher spectral purity and better recognition of endmembers, making it more suitable for studying dry–hot valley areas. (3) Estimates based on the NDVI-DFI ternary linear mixed model revealed significant seasonal shifts between PV and NPV, especially in valleys and lowlands. From the rainy to the dry season, the proportion of NPV increased from 23.37% to 35.52%, covering an additional 502.96 km². In summary, these findings underscore the substantial seasonal variations in f_PV and f_NPV, particularly in low-altitude regions along the valley, highlighting the dynamic nature of vegetation in dry–hot environments. Full article

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29 pages, 17899 KiB

Open AccessArticle

Geospatial Multi-Hazard Assessment for Gyeonggi-do Province, South Korea Subjected to Earthquake

by Han-Saem Kim and Mingi Kim

ISPRS Int. J. Geo-Inf. 2024, 13(12), 439; https://doi.org/10.3390/ijgi13120439 - 5 Dec 2024

Abstract

The increasing frequency of earthquake events worldwide, particularly in South Korea, necessitates detailed seismic hazard assessments to mitigate the risks to urban infrastructure. This study addresses this pressing need by developing a comprehensive multi-hazard assessment framework specific to the Gyeonggi-do Province. By leveraging [...] Read more.

The increasing frequency of earthquake events worldwide, particularly in South Korea, necessitates detailed seismic hazard assessments to mitigate the risks to urban infrastructure. This study addresses this pressing need by developing a comprehensive multi-hazard assessment framework specific to the Gyeonggi-do Province. By leveraging advanced geospatial computation techniques and geographic information systems, this study integrated geotechnical data, terrain information, and building inventories to evaluate seismic site effects, earthquake-induced landslide hazards, and structural vulnerability. This method uses geostatistical methods to construct geotechnical spatial grids that correlate site-specific seismic responses to potential hazards. The key findings revealed significant variations in seismic site responses owing to local subsurface characteristics, emphasizing the importance of site-specific seismic hazard maps for urban disaster preparedness. The framework’s effectiveness was validated by analyzing the 2017 Pohang earthquake, which demonstrated a strong correlation between predicted and observed damage. This study highlights the importance of ongoing seismic hazard assessment methodology development and advocates interdisciplinary collaboration to improve urban resilience, ultimately protecting communities from the impacts of future earthquakes. 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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23 pages, 14457 KiB

Open AccessArticle

Riders Under the Heat: Exploring the Impact of Extreme Heat on the Integration of Bike-Sharing and Public Transportation in Shenzhen, China

by Xin Wang, Rui Xue, Ming Lu and Jiangyue Wu

ISPRS Int. J. Geo-Inf. 2024, 13(12), 438; https://doi.org/10.3390/ijgi13120438 - 5 Dec 2024

Abstract

Bike-sharing (BS) systems provide a widely used and convenient feeder mode for connecting to public transportation (PT) and is seen as an effective solution to the first- and last-mile problem. Amidst the critical challenges posed by global climate change and rising temperatures, enhancing [...] Read more.

Bike-sharing (BS) systems provide a widely used and convenient feeder mode for connecting to public transportation (PT) and is seen as an effective solution to the first- and last-mile problem. Amidst the critical challenges posed by global climate change and rising temperatures, enhancing the resilience of the BS and PT integration is essential to ensure sustainable urban mobility and adapt to increasing climate variability, yet empirical studies in this area remain limited. This study analyzes BS-PT integration usage under extreme heats events, focusing on a Chinese mega city’s bike-sharing system. By defining extreme heat using the heat index, a more accurate measure of heat perception is established. We carefully categorize heatwaves based on the duration and temperature context of extreme heat to account for their potential impact on the integration’s response. Then, integrating multi-source big data and applying geographically weighted regression (GWR), we explore the spatial–temporal variations in the response of BS-PT integration ridership during different temperatures, and further identify key factors that contribute to the response of BS-PT integrated travel. Results show that extreme heat significantly influences BS-PT integration, with users showing a greater willingness to shift towards more integrated uses under extreme heat during non-summer seasons, compared to solely using bike-sharing, to avoid outdoor heat exposure. The temporal heterogeneity of the integrated trips is highest during extreme heat in non-summer and lowest under continuous extreme-heat periods. GWR spatial regression reveals that land-use characteristics significantly affect BS-PT integration resilience, with notable spatial differences in the influence of various factors, such as office density and entropy. These findings enhance our understanding of how climate change affects public transportation, providing urban planners and policymakers with valuable insights for improving the adaptability of urban mobility systems to climate change. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)

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18 pages, 4141 KiB

Open AccessArticle

The Coupling Coordination Relationship Between Urbanization and the Eco-Environment in Resource-Based Cities, Loess Plateau, China

by Shuaizhi Kang, Xia Jia, Yonghua Zhao, Manya Luo, Huanyuan Wang and Ming Zhao

ISPRS Int. J. Geo-Inf. 2024, 13(12), 437; https://doi.org/10.3390/ijgi13120437 - 4 Dec 2024

Abstract

Resource-based cities face numerous sustainability challenges, making the coupled and coordinated relationship between urbanization and the eco-environment critical for sustainable development strategies. The Loess Plateau is an essential energy base and ecologically fragile area in China, holding unique and significant research value. This [...] Read more.

Resource-based cities face numerous sustainability challenges, making the coupled and coordinated relationship between urbanization and the eco-environment critical for sustainable development strategies. The Loess Plateau is an essential energy base and ecologically fragile area in China, holding unique and significant research value. This research employed the Remote Sensing Ecological Index (RSEI) and the Compound Night Light Index (CNLI), based on MODIS and night light data, to investigate the socio-economic development and eco-environmental changes across 25 resource-based cities on the Loess Plateau (LP) in China over the past 20 years. The Coupling Coordination Degree Model (CCDM) and Multi-Scale Geographically Weighted Regression (MGWR) were utilized to assess the relationship between urbanization and ecological factors. The average RSEI values for these cities ranged from 0.4524 to 0.4892 over the 20 years, reflecting an upward trend with a growth rate of 8.13%. Simultaneously, the average CNLI values ranged from 1.5700 to 6.0864, with a change of 4.5164. Over the past two decades, all cities in the study area experienced rapid urbanization and ecological development. The correlation between urbanization and ecological factors strengthened, alongside an increasing spatial heterogeneity. While the coupling coordination relationship in most cities showed improvement, many remained within the low to middle grades. These findings enhance the understanding of the intricate relationships between urbanization and ecology, offering valuable insights for policy-making aimed at creating sustainable and livable resource-based cities. Full article

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

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23 pages, 31851 KiB

Open AccessArticle

Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches

by Subbarayan Sathiyamurthi, Saravanan Subbarayan, Madhappan Ramya, Murugan Sivasakthi, Rengasamy Gobi, Saleh Qaysi, Sivakumar Praveen Kumar, Jinwook Lee, Nassir Alarifi, Mohamed Wahba and Youssef M. Youssef

ISPRS Int. J. Geo-Inf. 2024, 13(12), 436; https://doi.org/10.3390/ijgi13120436 - 3 Dec 2024

Abstract

Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote [...] Read more.

Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries. 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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18 pages, 3601 KiB

Open AccessArticle

Granularity Optimization of Travel Trajectory Based on Node2vec: A Case Study on Urban Travel Time Prediction

by Hui Dong, Xiao Pan and Xiao Chen

ISPRS Int. J. Geo-Inf. 2024, 13(12), 435; https://doi.org/10.3390/ijgi13120435 - 2 Dec 2024

Abstract

Intersections are known to cause significant changes in traffic states. However, existing link-level trajectory optimization methods often overlook intersection information, making it challenging to preserve key traffic state features during the optimization process. To address this limitation, a novel approach is proposed that [...] Read more.

Intersections are known to cause significant changes in traffic states. However, existing link-level trajectory optimization methods often overlook intersection information, making it challenging to preserve key traffic state features during the optimization process. To address this limitation, a novel approach is proposed that integrates node2vec and K-means algorithms. First, the role of intersections in linking road segments is considered. The node2vec algorithm is employed to capture the deep spatial similarity between links while weakening the adjacency relationship between links before and after intersections. This process generates feature representations for each link. Subsequently, clustering centers are initialized at the intersections, and K-means clustering is applied based on these link feature representations. Through this method, consecutive links within a trajectory that belong to the same cluster are merged, thus optimizing the granularity of the trajectory. Finally, experimental analysis and validation are conducted using link-level travel trajectory data from Shenzhen. The results demonstrate that, under optimal conditions, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) values are reduced by 8.91%, 9.44%, and 8.96%, respectively, while computational efficiency is increased by 30.08%. The proposed trajectory granularity optimization method, which accounts for the existence of intersections, not only effectively retains the key traffic state features from the original trajectory but also significantly reduces training time while improving the model’s prediction accuracy. Full article

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21 pages, 8323 KiB

Open AccessArticle

A Dynamic Algorithm for Measuring Pedestrian Congestion and Safety in Urban Alleyways

by Jiyoon Lee and Youngok Kang

ISPRS Int. J. Geo-Inf. 2024, 13(12), 434; https://doi.org/10.3390/ijgi13120434 - 2 Dec 2024

Abstract

This study presents an algorithm for measuring Pedestrian Congestion and Safety on alleyways, wherein pedestrians and vehicles share limited space, making traditional pedestrian density metrics inadequate. The primary objective is to provide a more accurate assessment of congestion and safety in these shared [...] Read more.

This study presents an algorithm for measuring Pedestrian Congestion and Safety on alleyways, wherein pedestrians and vehicles share limited space, making traditional pedestrian density metrics inadequate. The primary objective is to provide a more accurate assessment of congestion and safety in these shared spaces by incorporating both pedestrian and vehicle interactions, unlike traditional methods that focus solely on pedestrians, regardless of road type. Pedestrian Congestion was calculated using Time to Collision (TTC)-based safety occupation areas, while Pedestrian Safety was assessed by accounting for both physical and psychological safety through proxemics, which measures personal space violations. The algorithm dynamically adapts to changing vehicle and pedestrian movements, providing a more accurate assessment of congestion compared to existing methods. Statistical validation through t-tests and K-S (Kolmogorov–Smirnov) tests confirmed significant differences between the proposed method and traditional pedestrian density metrics, while Bland–Altman analysis demonstrated agreement between the two methods. The experimental results reveal that Pedestrian Congestion and Safety varied with time and location, capturing the spatio-temporal characteristics of alleyways. Visual comparisons of Pedestrian Congestion, Safety, and Density further validated that the proposed algorithm provides a more accurate reflection of real-world conditions compared to traditional pedestrian density metrics. These findings highlight the algorithm’s ability to measure real-time changes in congestion and safety, incorporate psychological discomfort into safety calculations, and offer a comprehensive analysis by considering both pedestrian and vehicle interactions. Full article

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

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21 pages, 59527 KiB

Open AccessArticle

Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection Models

by Xiao Wang, Haizhong Qian, Limin Xie, Xu Wang and Bohao Li

ISPRS Int. J. Geo-Inf. 2024, 13(12), 433; https://doi.org/10.3390/ijgi13120433 - 2 Dec 2024

Abstract

The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it [...] Read more.

The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it is difficult to describe them using unified rules, which has always limited the quality and automation level of building shape recognition. In response to the above issues, by introducing object detection technology in computer vision, this article proposes a building shape recognition and classification method based on the YOLO object detection model. Firstly, for different types of buildings, four levels of building training data samples are constructed, and YOLOv5, YOLOv8, YOLOv9, and YOLOv9 integrating attention modules are selected for training. The trained models are used to test the shape judgment of buildings in the dataset and verify the learning effectiveness of the models. The experimental results show that the YOLO model can accurately classify and locate the shape of buildings, and its recognition and detection effect have the ability to simulate advanced human visual cognition, which provides a new solution for the fuzzy shape recognition of buildings with complex outlines and local deformation. Full article

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20 pages, 5601 KiB

Open AccessArticle

Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing

by Jin Zou, Xun Zhang, Yangxiao Cong, Zhentong Gao and Jinlian Shi

ISPRS Int. J. Geo-Inf. 2024, 13(12), 432; https://doi.org/10.3390/ijgi13120432 - 2 Dec 2024

Abstract

The layout and site selection strategy of commercial facilities are crucial for both enterprise performance and market image, while also significantly impacting the overall planning of urban commercial environments. However, conventional methods of choosing sites sometimes depend on outdated management information systems or [...] Read more.

The layout and site selection strategy of commercial facilities are crucial for both enterprise performance and market image, while also significantly impacting the overall planning of urban commercial environments. However, conventional methods of choosing sites sometimes depend on outdated management information systems or static statistical models, which may not take into account all relevant factors and have poor data quality. By utilizing geographical big data and geographical artificial intelligence, this study improves the viability of commercial layout and site selection methods. This study utilizes mobile phone signaling data from Beijing combined with point-of-interest (POI) data from within the Sixth Ring Road of Beijing to identify user behaviors using algorithms. Through a combination of BiLSTM-RF and reinforcement learning algorithms, a population location prediction algorithm is constructed to address the issues of inaccurate and outdated population flow data in commercial site selection. The forecast distribution has a high level of accuracy, with a prediction accuracy rate of 73.2%. Additionally, based on geographical big data, the urban landscape is reconstructed to create a 3D model of Beijing. An immersive interactive commercial site selection system is implemented using the Unreal Engine. Full article

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13 pages, 8834 KiB

Open AccessArticle

Preserving Spatial Patterns in Point Data: A Generalization Approach Using Agent-Based Modeling

by Martin Knura and Jochen Schiewe

ISPRS Int. J. Geo-Inf. 2024, 13(12), 431; https://doi.org/10.3390/ijgi13120431 - 30 Nov 2024

Abstract

Visualization and interpretation of user-generated spatial content such as Volunteered Geographic Information (VGI) is challenging because it combines enormous data volume and heterogeneity with a spatial bias. When dealing with point data on a map, these characteristics can lead to point clutter, reducing [...] Read more.

Visualization and interpretation of user-generated spatial content such as Volunteered Geographic Information (VGI) is challenging because it combines enormous data volume and heterogeneity with a spatial bias. When dealing with point data on a map, these characteristics can lead to point clutter, reducing the readability of the map product and misleading users to false interpretations of patterns in the data, e.g., regarding specific clusters or extreme values. With this work, we provide a framework that is able to generalize point data, preserving spatial clusters and extreme values simultaneously. The framework consists of an agent-based generalization model using predefined constraints and measures. We present the architecture of the model and compare the results with methods focusing on extreme value preservation as well as clutter reduction. As a result, we can state that our agent-based model is able to preserve elementary characteristics of point datasets, such as the point density of clusters, while also retaining the existing extreme values in the data. Full article

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ISPRS International Journal of Geo-Information

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