Journal Description
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information
is an international, peer-reviewed, open access journal on geo-information. The journal is owned by the International Society for Photogrammetry and Remote Sensing (ISPRS) and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), GeoRef, PubAg, dblp, Astrophysics Data System, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Geography, Physical) / CiteScore - Q1 (Geography, Planning and Development)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 35.5 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.4 (2022);
5-Year Impact Factor:
3.5 (2022)
Latest Articles
A Comprehensive Overview Regarding the Impact of GIS on Property Valuation
ISPRS Int. J. Geo-Inf. 2024, 13(6), 175; https://doi.org/10.3390/ijgi13060175 (registering DOI) - 25 May 2024
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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
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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.
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Open AccessArticle
Data-Driven Geofencing Design for Point-Of-Interest Notifiers Utilizing Genetic Algorithm
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Iori Sasaki, Masatoshi Arikawa, Min Lu, Tomihiro Utsumi and Ryo Sato
ISPRS Int. J. Geo-Inf. 2024, 13(6), 174; https://doi.org/10.3390/ijgi13060174 (registering DOI) - 25 May 2024
Abstract
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
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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.
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Open AccessArticle
Evaluating School Location Based on a Territorial Spatial Planning Knowledge Graph
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Xiankang Xu, Jian Hao and Jingwei Shen
ISPRS Int. J. Geo-Inf. 2024, 13(6), 173; https://doi.org/10.3390/ijgi13060173 - 24 May 2024
Abstract
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
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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.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
Open AccessArticle
Dynamic Perception-Based Vehicle Trajectory Prediction Using a Memory-Enhanced Spatio-Temporal Graph Network
by
Zhiming Gui, Xin Wang and Wenzheng Li
ISPRS Int. J. Geo-Inf. 2024, 13(6), 172; https://doi.org/10.3390/ijgi13060172 - 24 May 2024
Abstract
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.
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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.
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(This article belongs to the Special Issue Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI)
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Open AccessArticle
Assessing Risks in Cross-Regional Tourism Corridors: A Case Study of Tibetan Plateau Tourism
by
Ziqiang Li, Sui Ye and Jianchao Xi
ISPRS Int. J. Geo-Inf. 2024, 13(6), 171; https://doi.org/10.3390/ijgi13060171 - 23 May 2024
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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,
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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.
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Open AccessArticle
Challenges in Geocoding: An Analysis of R Packages and Web Scraping Approaches
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Virgilio Pérez and Cristina Aybar
ISPRS Int. J. Geo-Inf. 2024, 13(6), 170; https://doi.org/10.3390/ijgi13060170 - 23 May 2024
Abstract
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
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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.
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Open AccessArticle
Where Are Business Incubators Built? County-Level Spatial Distribution and Rationales Based on the Big Data of Chinese Yangtze River Delta Region
by
Tianhe Jiang and Zixuan Zhou
ISPRS Int. J. Geo-Inf. 2024, 13(6), 169; https://doi.org/10.3390/ijgi13060169 - 21 May 2024
Abstract
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
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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.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Open AccessArticle
The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics
by
Sultan Alamri
ISPRS Int. J. Geo-Inf. 2024, 13(6), 168; https://doi.org/10.3390/ijgi13060168 - 21 May 2024
Abstract
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
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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.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Open AccessArticle
Community Quality Evaluation for Socially Sustainable Regeneration: A Study Using Multi-Sourced Geospatial Data and AI-Based Image Semantic Segmentation
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Jinliu Chen, Wenquan Gan, Ning Liu, Pengcheng Li, Haoqi Wang, Xiaoxin Zhao and Di Yang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 167; https://doi.org/10.3390/ijgi13050167 - 20 May 2024
Abstract
The Chinese urban regeneration movement underscores a “people-oriented” paradigm, aimed at addressing urban challenges stemming from rapid prior urbanization, while striving for high-quality and sustainable urban development. At the community level, fostering quality through a socially sustainable perspective (SSP) is a pivotal strategy
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The Chinese urban regeneration movement underscores a “people-oriented” paradigm, aimed at addressing urban challenges stemming from rapid prior urbanization, while striving for high-quality and sustainable urban development. At the community level, fostering quality through a socially sustainable perspective (SSP) is a pivotal strategy for people-oriented urban regeneration. Nonetheless, explorations of community quality assessments grounded in an SSP have been notably scarce in recent scholarly discourse. This study pioneers a multidimensional quantitative model (MQM) for gauging community quality, leveraging diverse geospatial data sources from the SSP framework. The MQM introduces an evaluative framework with “Patency, Convenience, Comfort, and Safety” as primary indicators, integrating multi-sourced data encompassing the area of interest (AOI), Point of Interest (POI), Weibo check-ins, and Dianping data. The model’s efficacy is demonstrated through a case study in the Gusu district, Suzhou. Furthermore, semantic analysis of the Gusu district’s street view photos validates the MQM results. Our findings reveal the following: (1) AI-based semantic analysis accurately verifies the validity of MQM-generated community quality measurements, establishing its robust applicability with multi-sourced geospatial data; (2) the community quality distribution in Gusu district is notably correlated with the urban fabric, exhibiting lower quality within the ancient town area and higher quality outside it; and (3) communities of varying quality coexist spatially, with high- and low-quality communities overlapping in the same regions. This research pioneers a systematic, holistic methodology for quantitatively measuring community quality, laying the groundwork for informed urban regeneration policies, planning, and place making. The MQM, fortified by multi-sourced geospatial data and AI-based semantic analysis, offers a rigorous foundation for assessing community quality, thereby guiding socially sustainable regeneration initiatives and decision making at the community scale.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Open AccessArticle
Improved A* Navigation Path-Planning Algorithm Based on Hexagonal Grid
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Zehua An, Xiaoping Rui and Chaojie Gao
ISPRS Int. J. Geo-Inf. 2024, 13(5), 166; https://doi.org/10.3390/ijgi13050166 - 16 May 2024
Abstract
Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes
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Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes and turns. Addressing these limitations, an enhanced A* algorithm was proposed using regular hexagonal grid mapping. First, the approach to map modeling using hexagonal grids was described. Subsequently, the A* algorithm was refined by optimizing the calculation of movement costs, thus allowing the algorithm to integrate environmental data more effectively and flexibly adjust node costs while ensuring path optimality. A quantitative method was also introduced to assess map complexity and adaptive heuristics that decrease the number of traversed nodes and increase the search speed. Moreover, a turning penalty measure was implemented to minimize unnecessary turns on the planned paths. Simulation results confirmed that the improved A* algorithm exhibits superior performance, which can dynamically adjust movement costs, enhance search efficiency, reduce turns, improve overall path planning quality, and solve critical path planning issues in navigation systems, greatly aiding the development and design of these systems and making them better suited to meet modern navigation requirements.
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(This article belongs to the Topic Geocomputation and Artificial Intelligence for Mapping)
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Open AccessArticle
A Question and Answering Service of Typhoon Disasters Based on the T5 Large Language Model
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Yongqi Xia, Yi Huang, Qianqian Qiu, Xueying Zhang, Lizhi Miao and Yixiang Chen
ISPRS Int. J. Geo-Inf. 2024, 13(5), 165; https://doi.org/10.3390/ijgi13050165 - 14 May 2024
Abstract
A typhoon disaster is a common meteorological disaster that seriously impacts natural ecology, social economy, and even human sustainable development. It is crucial to access the typhoon disaster information, and the corresponding disaster prevention and reduction strategies. However, traditional question and answering (Q&A)
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A typhoon disaster is a common meteorological disaster that seriously impacts natural ecology, social economy, and even human sustainable development. It is crucial to access the typhoon disaster information, and the corresponding disaster prevention and reduction strategies. However, traditional question and answering (Q&A) methods exhibit shortcomings like low information retrieval efficiency and poor interactivity. This makes it difficult to satisfy users’ demands for obtaining accurate information. Consequently, this work proposes a typhoon disaster knowledge Q&A approach based on LLM (T5). This method integrates two technical paradigms of domain fine-tuning and retrieval-augmented generation (RAG) to optimize user interaction experience and improve the precision of disaster information retrieval. The process specifically includes the following steps. First, this study selects information about typhoon disasters from open-source databases, such as Baidu Encyclopedia and Wikipedia. Utilizing techniques such as slicing and masked language modeling, we generate a training set and 2204 Q&A pairs specifically focused on typhoon disaster knowledge. Second, we continuously pretrain the T5 model using the training set. This process involves encoding typhoon knowledge as parameters in the neural network’s weights and fine-tuning the pretrained model with Q&A pairs to adapt the T5 model for downstream Q&A tasks. Third, when responding to user queries, we retrieve passages from external knowledge bases semantically similar to the queries to enhance the prompts. This action further improves the response quality of the fine-tuned model. Finally, we evaluate the constructed typhoon agent (Typhoon-T5) using different similarity-matching approaches. Furthermore, the method proposed in this work lays the foundation for the cross-integration of large language models with disaster information. It is expected to promote the further development of GeoAI.
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(This article belongs to the Special Issue Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI)
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Open AccessArticle
Changes in the 19th Century Cultural Landscape with Regard to City Rights in Western Poland
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Dariusz Lorek and Tymoteusz Horbiński
ISPRS Int. J. Geo-Inf. 2024, 13(5), 164; https://doi.org/10.3390/ijgi13050164 - 14 May 2024
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This research study focuses on determining the spatial transformations taking place in selected areas in the context of administrative changes in the 19th century (in the context of city rights) using the example of three neighboring places in western Poland. The occurrence of
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This research study focuses on determining the spatial transformations taking place in selected areas in the context of administrative changes in the 19th century (in the context of city rights) using the example of three neighboring places in western Poland. The occurrence of both individual topographic features and the transformation of structures and spatial relations occurring in the studied area since the 19th century were considered. The source material included archival cartographic studies from six time periods and contemporary data resources. A significant part of the research concerned the development of the possibility of using and presenting the data in an interactive form. The most important functions include comparing three neighboring places at the same time. Programming activities focused on the implementation of all collected archive data in the form of rasters and the construction of a map service divided into three windows (taking into account the turning on of layers simultaneously for all windows). The Leaflet library was used to create the proposed map solution.
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Open AccessArticle
VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information
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Yinglong Wang, Xiaoxiong Liu, Minkun Zhao and Xinlong Xu
ISPRS Int. J. Geo-Inf. 2024, 13(5), 163; https://doi.org/10.3390/ijgi13050163 - 13 May 2024
Abstract
A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry
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A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance.
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(This article belongs to the Topic Artificial Intelligence in Navigation)
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Open AccessArticle
Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan
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Dimas Pradana Putra and Po-Chun Hsu
ISPRS Int. J. Geo-Inf. 2024, 13(5), 162; https://doi.org/10.3390/ijgi13050162 - 13 May 2024
Abstract
Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters
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Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.
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(This article belongs to the Topic Advances in Earth Observation and Geosciences)
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Open AccessArticle
Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models
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Anh Van Tran, Maria Antonia Brovelli, Khien Trung Ha, Dong Thanh Khuc, Duong Nhat Tran, Hanh Hong Tran and Nghi Thanh Le
ISPRS Int. J. Geo-Inf. 2024, 13(5), 161; https://doi.org/10.3390/ijgi13050161 - 11 May 2024
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The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we
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The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we assessed the land subsidence susceptibility in the Ca Mau Peninsula utilizing three boosting machine learning models: AdaBoost, Gradient Boosting, and Extreme Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), groundwater depth, digital terrain model (DTM), normalized vegetation index (NDVI), geology, soil composition, distance to roads, and distance to rivers and streams. The dataset includes 2011 points referenced from the Persistent Scattering SAR Interferometry (PSI) method, of which 1011 points are subsidence points and the remaining are non-subsidence points. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), specificity, sensitivity, and overall accuracy (ACC). The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC and ACC above 0.88 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau Peninsula. In addition, 31 subsidence points measured by leveling surveys between 2005 and 2020, provided by the Department of Survey, Mapping and Geographic Information Vietnam, were used for validating the land subsidence susceptibility from the XGB method. The findings indicate a 70.9% accuracy rate in predicting subsidence susceptibility compared to the leveling measurement points.
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Open AccessArticle
Towards Quality Management Procedures in 3D Cadastre
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Nenad Višnjevac, Mladen Šoškić and Rajica Mihajlović
ISPRS Int. J. Geo-Inf. 2024, 13(5), 160; https://doi.org/10.3390/ijgi13050160 - 9 May 2024
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The 3D cadastre presents a modern approach to the development of cadastral information systems, with the role of improving current cadastral systems and overcoming the challenges of a 2D-based approach. Technological advancements, standardization, and scientific research in recent decades have contributed to the
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The 3D cadastre presents a modern approach to the development of cadastral information systems, with the role of improving current cadastral systems and overcoming the challenges of a 2D-based approach. Technological advancements, standardization, and scientific research in recent decades have contributed to the development and definition of the 3D cadastre. This positioned the 3D cadastre as an integral part of the future of land administration. However, every country needs to define a solution for itself based on its own legal system and cadastral tradition, while at the same time relying on international standardization and research. Once a 3D cadastral system is developed, it is crucial to ensure the monitoring, evaluation, and maintenance of both the quality of the cadastral data and the system itself throughout its lifecycle. Since 3D cadastres involve geometric data, quality management procedures must address both geometric and alphanumeric data. In this paper, we analyze and present the quality management procedures that should be included during designing, implementing, and maintaining a 3D cadastral system. Some examples based on real cadastral data were used to emphasize the need for improvement in quality management. The presented quality management procedures require further development in order to meet country-specific requirements and to fully support the 3D cadastre information systems.
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Open AccessArticle
Total Least Squares Estimation in Hedonic House Price Models
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Wenxi Zhan, Yu Hu, Wenxian Zeng, Xing Fang, Xionghua Kang and Dawei Li
ISPRS Int. J. Geo-Inf. 2024, 13(5), 159; https://doi.org/10.3390/ijgi13050159 - 8 May 2024
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In real estate valuation using the Hedonic Price Model (HPM) estimated via Ordinary Least Squares (OLS) regression, subjectivity and measurement errors in the independent variables violate the Gauss–Markov theorem assumption of a non-random coefficient matrix, leading to biased parameter estimates and incorrect precision
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In real estate valuation using the Hedonic Price Model (HPM) estimated via Ordinary Least Squares (OLS) regression, subjectivity and measurement errors in the independent variables violate the Gauss–Markov theorem assumption of a non-random coefficient matrix, leading to biased parameter estimates and incorrect precision assessments. In this contribution, the Errors-in-Variables model equipped with Total Least Squares (TLS) estimation is proposed to address these issues. It fully considers random errors in both dependent and independent variables. An iterative algorithm is provided, and posterior accuracy estimates are provided to validate its effectiveness. Monte Carlo simulations demonstrate that TLS provides more accurate solutions than OLS, significantly improving the root mean square error by over 70%. Empirical experiments on datasets from Boston and Wuhan further confirm the superior performance of TLS, which consistently yields a higher coefficient of determination and a lower posterior variance factor, which shows its more substantial explanatory power for the data. Moreover, TLS shows comparable or slightly superior performance in terms of prediction accuracy. These results make it a compelling and practical method to enhance the HPM.
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Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization
by
Supattra Puttinaovarat, Supaporn Chai-Arayalert and Wanida Saetang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 158; https://doi.org/10.3390/ijgi13050158 - 8 May 2024
Abstract
Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest
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Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.
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(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System
by
Tariq Alsahfi
ISPRS Int. J. Geo-Inf. 2024, 13(5), 157; https://doi.org/10.3390/ijgi13050157 - 8 May 2024
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Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic
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Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies.
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A Multi-Feature Fusion Method for Urban Functional Regions Identification: A Case Study of Xi’an, China
by
Zhuo Wang, Jianjun Bai and Ruitao Feng
ISPRS Int. J. Geo-Inf. 2024, 13(5), 156; https://doi.org/10.3390/ijgi13050156 - 7 May 2024
Abstract
Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field.
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Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi’an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi’an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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