ISPRS Int. J. Geo-Inf., Volume 13, Issue 6 (June 2024) – 20 articles

Land cover and its changes over time are significant for better understanding the Earth’s fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs. Full article

The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan’s new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran’s index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities. Full article

Discontinuity is a key element used by geoscientists and civil engineers to characterize rock masses. The traditional approach to detecting and measuring rock discontinuity relies on fieldwork, which poses dangers to human life. Photogrammetric pattern recognition and 3D measurement techniques offer new possibilities without direct contact with rock masses. This study proposes a new approach to detect discontinuities using close-range photogrammetric techniques and convolutional neural networks (CNNs) trained on a small amount of data. Investigations were conducted on basalts in Bala, Ankara, Türkiye. A total of 34 multi-view images were collected with a remotely piloted aircraft system (RPAS), and discontinuity lines were manually delineated on a point cloud generated from these images. The lines were back-projected onto the raw images to increase the amount of data, a process we call multi-view (3D) augmentation. We further evaluated radiometric and geometric augmentation methods, the contribution of multi-view augmentation to the proposed model, and the transfer learning performance of six different CNN architectures. The highest performance was achieved with U-Net + SE-ResNeXt-50 with an F1-score of 90.6%. The CNN model trained from scratch with local features also yielded a similar F1-score (91.7%), which is the highest performance reported in the literature. Full article

In the face of increasing demands for tasks such as mountain rescue, geological exploration, and military operations in complex wilderness environments, planning an efficient walking route is crucial. To address the inefficiency of traditional two-dimensional path planning, this paper proposes a two-stage path planning algorithm. First, an improved Probabilistic Roadmap (PRM) algorithm is used to quickly and roughly determine the initial path. Then, the morphological dilation is applied to process the grid points of the initial path, retaining the surrounding area of the initial path for a precise positioning of the search range. Finally, the idea of the A^∗ algorithm is applied to achieve precise path planning in the refined search range. During the process of constructing the topology map, we utilized parallelization acceleration strategies to expedite the graph construction. In order to verify the effectiveness of the algorithm, we used terrain data to construct a wilderness environment model, and tests were conducted on off-road path planning tasks with different terrains and distances. The experimental results show a substantial enhancement in the computational efficiency of the proposed algorithm relative to the conventional A^∗ algorithm by 30 to 60 times. Full article

With the increase in urban development intensity, the urban climate has become an important factor affecting sustainable development. The role of urban ventilation corridors in improving urban climate has received widespread attention. Urban ventilation identification and planning based on morphological methods have been initially applied. Traditional morphological methods do not adequately consider the dynamic process of air flow, resulting in a rough evaluation of urban ventilation patterns. This study proposes a new urban-scale ventilation corridor identification method that integrates the Lattice Boltzmann method and the K-means algorithm. Taking Wuhan, China as the research area, an empirical study in different wind directions was conducted on a 20 m grid. The results showed that three levels of ventilation corridors ($245.47 {\mathrm{k}\mathrm{m}}^2 \mathrm{in} \mathrm{total}$) and two levels of ventilation obstruction areas ($658.09 {\mathrm{k}\mathrm{m}}^2 \mathrm{in} \mathrm{total}$) were identified to depict the ventilation pattern of Wuhan’s central urban area. The method proposed in this study can meet the needs of urban-scale ventilation corridor identification in terms of spatial coverage, spatial distribution rate and dynamic analysis. Compared with the classic least cumulative ventilation cost method, the method proposed in this study can provide more morphologic details of the ventilation corridors. This plays a very important role in urban planning based on urban ventilation theory. Full article

Safety from crime is a fundamental human need. In Maslow’s hierarchy, safety is one of the foundational needs of well-being. The built environment should be safe to use at all times of the day and for all groups of people. After dark, the appearance of the outdoor environment changes dramatically, and this could impact the opportunities for crime. This study investigated the impact of daylight on the rates of different types of crime by comparing the crime rates during selected periods of daylight and darkness. The study used records of crime data from the Metropolitan Police Service. By studying crimes in the week on either side of the twice-yearly clock change, it is possible to compare periods that are dark in one week and light in the other at the same clock time. Where the time at which the crime took place was known, and using the GPS coordinates of the specific crime, the solar altitude was calculated and used to determine if it was light or dark at the time of the crime. A similar calculation was used to see if the crime would have been in the dark or light in the week on the other side of the clock change. The headline result is that there was 4.8% (OR 1.07) more crime in the dark periods than the light ones. However, this increase was not uniform across all crime types, and there were some further complications in some results due to potential changes in the behavior of some victims after dark. For the crimes of theft from a person and robbery of personal property, there was a significant increase during the dark period. The availability of light had an impact on the rate of certain crimes. Whilst this does not provide any information about the impact of street lighting on crime, it does provide some idea of by how much crime could be reduced if better lighting was provided. Full article

To control the growth of CO₂ emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies. Full article

Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a geospatial approach to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mitigating floods and preventing overtopping failure at Wivenhoe Dam. Using QGIS software and a 25 m resolution DEM from the Queensland Spatial Catalogue ‘QSpatial’ website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied on several dam wall options to identify the cost-effective and optimal one based on the dam wall dimensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria yielding the optimal choice. The study provides insights into the feasibility and effectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation plans for the asset owners. Full article

With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study focuses on Lanzhou City as a case study to deeply analyze the spatiotemporal distribution and evolution of color steel plate buildings. Utilizing data extracted from Google imagery and GF-2 satellite images of the built-up areas in Lanzhou, spatial statistical and analytical methods such as centroid analysis, compactness index, and patch density are applied. Systematic analysis is conducted across different time periods and spatial scales to examine the evolution of indicators, including quantity, centroid distribution, spatial clustering, and distribution direction. The results show that from 2013 to 2021, the prevalence of color steel buildings in Lanzhou city initially increased and then decreased, and the number peaked in 2017, but there is a significant difference between distinct areas in the urban area. By quantitatively analyzing the spatial and temporal evolution characteristics of color steel plate buildings, this study reveals the important role it plays in promoting the urbanization process and provides a scientific basis for relevant planning decisions. Full article

Propelled by emerging technologies such as artificial intelligence and deep learning, the essence and scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support for revealing and deepening the essence of maps. In this study, CiteSpace was used to examine the confluence of cartography and neural networks over the past decade (2013–2023), thus revealing the prevailing research trends and cutting-edge investigations in the field of machine learning and its application in mapping. In addition, this analysis included the systematic categorization of knowledge clusters arising from the fusion of cartography and neural networks, which was followed by the discernment of pivotal clusters in the field of knowledge mapping. Crucially, this study diligently identified the critical studies (milestones) that have made significant contributions to the development of these elucidated clusters. Timeline analysis was used to track these studies’ origins, evolution, and current status. Finally, we constructed collaborative networks among the contributing authors, journals, institutions, and countries. This mapping aids in identifying and visualizing the primary contributing factors of the evolution of knowledge mapping encompassing cartography and neural networks, thus facilitating interdisciplinary and multidisciplinary research and investigations. Full article

The problems of administrations based simply on administrative units that do not consider the operational purposes of the system have been consistently discussed. For example, in the Republic of Korea, sex offenders’ information is distributed via physical mail only in a few regions, a practice that is too rigidly based on the boundaries of the administrative ‘Dong’ of the offender’s residence. This implies that citizens in an adjacent building will not be notified if their Dong is different. Therefore, this study analyzed the problems of an administrative system that does not consider its realistic scope by using the case study of sex offender notifications. By expanding the distance from children and youth grids, we ascertained the extent of the problems with sex offender notifications. Additionally, to determine whether these problems have occurred by chance at a specific point in time or if there has been a fundamental limitation in the system, the Monte Carlo simulation was applied to compare the actual and random data of residences. Full article

In recent years, the rapid development of autonomous driving and intelligent driver assistance has brought about urgent demands on high-precision road maps. However, traditional road map production methods mainly rely on professional survey technologies, such as remote sensing and mobile mapping, which suffer from high costs, object occlusions, and long updating cycles. In the era of ubiquitous mapping, crowdsourced trajectory data offer a new and low-cost data resource for the production and updating of high-precision road maps. Meanwhile, as key nodes in the transportation network, maintaining the currency and integrity of road intersection data is the primary task in enhancing map updates. In this paper, we propose a novel approach for detecting road intersections based on crowdsourced trajectory data by introducing an attention mechanism and modifying the loss function in the YOLOv5 model. The proposed method encompasses two key steps of training data preparation and improved YOLOv5s model construction. Multi-scale training processing is first adopted to prepare a rich and diverse sample dataset, including various kinds and different sizes of road intersections. Particularly to enhance the model’s detection performance, we inserted convolutional attention mechanism modules into the original YOLOv5 and integrated other alternative confidence loss functions and localization loss functions. The experimental results demonstrate that the improved YOLOv5 model achieves detection accuracy, precision, and recall rates as high as 97.46%, 99.57%, and 97.87%, respectively, outperforming other object detection models. Full article

In the face of pressing global challenges such as climate change, socioeconomic inequalities, and rapid urbanization, ensuring sustainable development in the regions has become essential. The COVID-19 pandemic has highlighted how vulnerable cities are to unforeseen crises and underscored the urgent need for proactive urban planning strategies capable of navigating dynamic and unpredictable futures. In this context, the use of geographic information systems (GIS) offers researchers and decision makers a distinct advantage in the study of spatial data and enables the comprehensive study of spatial and temporal patterns in various disciplines, including real estate valuation. Central to the integration of modern technology into real estate valuation is the need to mitigate the inherent subjectivity of traditional valuation methods while increasing efficiency through the use of mass appraisal techniques. This study draws on extensive academic literature comprising 103 research articles published between 1993 and January 2024 to shed light on the multifaceted application of GISs in real estate valuation. In particular, three main areas are addressed: (1) hedonic models, (2) artificial intelligence (AI), and mathematical appraisal models. This synthesis emphasizes the interdependence of numerous societal challenges and highlights the need for interdisciplinary collaboration to address them effectively. In addition, this study provides a repertoire of methodologies that underscores the potential of advanced technologies, including artificial intelligence, GISs, and satellite imagery, to improve the subjectivity of traditional valuation approaches and thereby promote greater accuracy and productivity in real estate valuation. By integrating GISs into real estate valuation methodologies, stakeholders can navigate the complexity of urban landscapes with greater precision and promote equitable valuation practices that are conducive to sustainable urban development. Full article

This study proposes a method for generating geofences driven by GPS trajectory data to realize scalable point-of-interest (POI) notifiers, encouraging walking tourists to discover new local spots. The case study revealed that manual geofence settings degrade the location relevance and user coverage—key objectives of POI notifiers—and hinder the scalability and reliability of services. The formalization presented computationally equips geofence designers with practical solutions through two implementations based on prior GPS trajectory logs: (1) a multiobjective genetic algorithm that suggests cost-effective geofences by providing trade-off visualizations and (2) a user coverage-penalized genetic algorithm that determines an optimal geofence based on the designers’ expectations. The feasibility and stability of the proposed implementations were tested in areas with varying tourist flow patterns. A comparative survey among manual settings, settings incorporating a reliability simulation, and data-driven settings demonstrates significant performance improvements for geofence services. Full article

The reasonable spatial planning of primary and secondary schools is an important factor in education development. In spatial planning, there are many models for the locations of primary and secondary schools; however, few quantitative evaluation models are available. Therefore, based on the many factors affecting the layout planning of primary and secondary schools, a knowledge graph of territorial spatial planning that considers the topological relationship, direction relationship and metric relationship in spatial planning is designed and constructed. A school location evaluation model based on the knowledge graph of territorial spatial planning is proposed. The model combines many factors of the locations of schools, such as the service population, the impact of factories on schools, the adjacency and centrality of school plots, terrain and existing schools in the region, to quantitatively evaluate whether schools are reasonably located within a region. This study focuses on the Guangyang Island area in Chongqing, China, exploring the superiority and rationality of the planned land use for primary and secondary schools within the region. By analyzing the top three and bottom three ranked schools in conjunction with the actual conditions of the site, and comparing them with AHP hierarchical analysis and ArcGIS modelling research, the study concludes that the results of this model are highly reasonable within the scope of China’s territorial spatial planning. Full article

In the realm of intelligent transportation systems, accurately predicting vehicle trajectories is paramount for enhancing road safety and optimizing traffic flow management. Addressing the impacts of complex traffic environments and efficiently modeling the diverse behaviors of vehicles are the key challenges at present. To achieve precise prediction of vehicle trajectories, it is essential to fully consider the dynamic changes in traffic conditions and the long-term dependencies of time-series data. In response to these challenges, we propose the Memory-Enhanced Spatio-Temporal Graph Network (MESTGN), an innovative model that integrates a Spatio-Temporal Graph Convolutional Network (STGCN) with an attention-enhanced Long Short-Term Memory (LSTM)-based sequence to sequence (Seq2Seq) encoder–decoder structure. MESTGN utilizes STGCN to capture the complex spatial dependencies between vehicles and reflects the interactions within the traffic network through road traffic data and network topology, which significantly influences trajectory prediction. Additionally, the model focuses on historical vehicle trajectory data points using an attention-weighted mechanism under a traditional LSTM prediction architecture, calculating the importance of critical trajectory points. Finally, our experiments conducted on the urban traffic dataset ApolloSpace validate the effectiveness of our proposed model. We demonstrate that MESTGN shows a significant performance improvement in vehicle trajectory prediction compared with existing mainstream models, thereby confirming its increased prediction accuracy. Full article

Due to the frequent impact of external risks, scientific tourism risk assessment has become the primary task to be implemented in the process of tourism development. Especially with the development of self-driving travel, cross-regional tourism corridors have become an important tourism carrier. However, compared to traditional fixed-location tourism, cross-regional tourism introduces a more intricate landscape of risks. Therefore, there is a pressing need to assess the tourism risks inherent in these corridors. There are many cross-regional tourism corridors in the Tibetan Plateau, but the natural environment of the Tibetan Plateau brings great risks to these tourism corridors. That is why this study focuses on the Tibetan Plateau’s tourism corridors, employing methodologies such as the Analytic Hierarchy Process, entropy weight method, geographic information systems (GIS) spatial analysis, and others to delve into their tourism risk profiles and the influencing factors. Our findings reveal elevated tourism risks across the Tibetan Plateau’s corridors, notably concentrated along the Yunnan–Tibet Line, north Sichuan–Tibet Line, Xinjiang–Tibet Line, Tangfan Ancient Road, Qinghai–Tibet Line, and south Sichuan–Tibet Line. Furthermore, Geodetector was employed to scrutinize the factors influencing tourism risk within the Tibetan Plateau’s corridors, identifying tourism resource endowment, geographical location, precipitation patterns, and economic foundations as primary influencers. Notably, the interaction between these factors exacerbates the overall tourism risk. These insights significantly contribute to the field of tourism risk research and provide a scientific basis for formulating robust tourism safety management strategies within the Tibetan Plateau region. Full article

Georeferenced data are crucial for addressing societal spatial challenges, as most corporate and governmental information is location-compatible. However, many open-source solutions lack automation in geocoding while ensuring quality. This study evaluates the functionalities of various R packages and their integration with external APIs for converting postal addresses into geographic coordinates. Among the fifteen R methods/packages reviewed, tidygeocoder stands out for its versatility, though discrepancies in processing times and missing values vary by provider. The accuracy was assessed by proximity to original dataset coordinates (Madrid street map) using a sample of 15,000 addresses. The results indicate significant variability in performance: MapQuest was the fastest, ArcGIS the most accurate, and Nominatim had the highest number of missing values. To address these issues, an alternative web scraping methodology is proposed, substantially reducing the error rates and missing values, but raising potential legal concerns. This comparative analysis highlights the strengths and limitations of different geocoding tools, facilitating better integration of geographic information into datasets for researchers and social agents. Full article

Business incubators (BIs) in China have predominantly exhibited a government-led characteristic, recently broadening their spatial and temporal scope and extending reach to the county level. Regarding the inadequacies of county-level analysis scale, this study leverages Points of Interest (POI) big data to overcome them. To comprehend the governmental rationale in the construction of BIs, we examine the evolution dynamics of BIs in conjunction with policies. An economic geography framework is developed, conceptualizing BIs as quasi-public goods and productive services, and incorporating considerations of county-level fiscal operations and industrial structures. Focusing on the Yangtze River Delta (YRD) region as a case study, our findings reveal that over 98% of County Administrative Units (CAUs) have built BIs. Using kernel density estimation and Moran’s I, the spatial patterns of CAUs are identified. The CAUs are further classified into three categories of economic levels using the k-means algorithm, uncovering differentiated relationships between industry, finance, and their respective BI. Additionally, we analyze the density relationship between BIs and other facilities at a micro-level, showcasing various site selection rationales. The discussions highlight that while BIs tend to align with wealthier areas and advanced industries, affluent CAUs offer location advantages on BIs, whereas less wealthy CAUs prioritize quantity for political achievements. This paper concludes with recommendations about aligning BIs based on conditions and outlooks on future research. Full article

Crowdsourced spatial analytics is a rapidly developing field that involves collecting and analyzing geographical data, utilizing the collective power of human observation. This paper explores the field of spatial data analytics and crowdsourcing and how recently developed tools, cloud-based GIS, and artificial intelligence (AI) are being applied in this domain. This paper examines and discusses cutting-edge technologies and case studies in different fields of spatial data analytics and crowdsourcing used in a wide range of industries and government departments such as urban planning, health, transportation, and environmental sustainability. Furthermore, by understanding the concerns associated with data quality and data privacy, this paper explores the potential of crowdsourced data while also examining the related problems. This study analyzes the obstacles and challenges related to “geospatial crowdsourcing”, identifying significant limitations and predicting future trends intended to overcome the related challenges. Full article