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Special Issue "Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI"

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 20 March 2024 | Viewed by 8957

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Special Issue Editors

Dr. Peng Peng

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: geographical information science; spatial and temporal information modelling; complex network analysis; maritime transportation; trajectory data mining
Special Issues, Collections and Topics in MDPI journals

Dr. Shu Wang

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: geographical information science; geographical knowledge graph; geographical information retrieval; geoparsing; data & knowledge formalization

Dr. Maryam Lotfian

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Guest Editor

Institute INSIT, School of Business and Engineering Vaud, University of Applied Sciences and Arts Western Switzerland, 1400 Yverdon-les-Bains, Switzerland
Interests: geographic information science; geospatial artificial intelligence; citizen science; open data; geospatial web; spatio-temporal modelling

Prof. Dr. Feng Lu

E-Mail Website
Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: geographical information science; spatio-temporal databases; geo-spatial data mining; machine learning; complex network analysis; NLP; computational transportation science
Special Issues, Collections and Topics in MDPI journals

Dr. Yunqiang Zhu

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: geographical information science; scientific data sharing; e-geoscience; ontology; knowledge graph; spatial information technology; automatic data matching

Special Issue Information

Dear Colleagues,

Despite its continuous and successful development, GIS should, on the one hand, integrate the novel possibilities offered by the extensive and promising development of sensor-based systems and AI resources and, on the other hand, respond to the novel and urgent needs required in the face of environmental challenges. Sensor-based systems have advanced significantly in recent years, and they can now collect large amounts of data from numerous sources, such as satellites, drones, and IoT devices. Similarly, AI resources have shown great potential to enhance GIS. AI algorithms can analyze large amounts of data, derive useful insights, and make predictions based on previous observations. With the gradual maturation of GIS core approaches, novel AI algorithms, and the emergence of big geographical datasets and sensor-based systems, new data representation forms such as knowledge graphs and interactive and visual systems have shown more intuitive and efficient advantages in the representation, exploration, mining, and analysis of complex geographical phenomena than conventional methods. Accordingly, this Special Issue invites innovative research works that integrate the new forms of data representation and modeling within theoretical, formal, and practical GIS solutions, as well as their application to urban and environmental applications in the AI era. Provided that they fit the scope of the call, the articles can cover, but are not limited to, the following themes:

Novel GIS data representations and structures
Sensor-based and real-time GIS
AI-based GIS models and languages
Integrated qualitative and quantitative approaches
Knowledge graph and innovative models
Geovisualization analytics
Innovative interfaces

Dr. Peng Peng
Dr. Shu Wang
Dr. Maryam Lotfian
Prof. Dr. Feng Lu
Dr. Yunqiang Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

GeoAI
knowledge graph
spatio-temporal models and interfaces
real-time GIS
geovisualization analytics

Published Papers (11 papers)

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Research

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24 pages, 7987 KiB

Open AccessArticle

Mapping Gross Domestic Product Distribution at 1 km Resolution across Thailand Using the Random Forest Area-to-Area Regression Kriging Model

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ISPRS Int. J. Geo-Inf. 2023, 12(12), 481; https://doi.org/10.3390/ijgi12120481 - 27 Nov 2023

Viewed by 289

Abstract

Accurate spatial distribution of gridded gross domestic product (GDP) data is crucial for revealing regional disparities within administrative units, thus facilitating a deeper understanding of regional economic dynamics, industrial distribution, and urbanization trends. The existing GDP spatial models often rely on prediction residuals for model evaluation or utilize residual distribution to improve the final accuracy, frequently overlooking the modifiable areal unit problem within residual distribution. This paper introduces a hybrid downscaling model that combines random forest and area-to-area kriging to map gridded GDP. Employing Thailand as a case study, GDP distribution maps were generated at a 1 km spatial resolution for the year 2015 and compared with five alternative downscaling methods and an existing GDP product. The results demonstrate that the proposed approach yields higher accuracy and greater precision in detailing GDP distribution, as evidenced by the smallest mean absolute error and root mean squared error values, which stand at USD 256.458 and 699.348 ten million, respectively. Among the four different sets of auxiliary variables considered, one consistently exhibited a higher prediction accuracy. This particular set of auxiliary variables integrated classification-based variables, illustrating the advantages of incorporating such integrated variables into modeling while accounting for classification characteristics. Full article

(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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14 pages, 3856 KiB

Open AccessArticle

Spatial Accessibility of Public Electric Vehicle Charging Services in China

Yu Chen

Yuehong Chen

and

Yuqi Lu

ISPRS Int. J. Geo-Inf. 2023, 12(12), 478; https://doi.org/10.3390/ijgi12120478 - 25 Nov 2023

Viewed by 308

Abstract

Decarbonizing the transport sector using electric vehicles (EVs) is a vital pathway for China to achieve the carbon peak and carbon neutrality goals. Despite the unprecedented growth of EV diffusion in China, little information is available for the spatial accessibility of public electric vehicle charging services (EVCSs). This study developed an applicable accessibility measurement framework to examine the city-level accessibility of EVCSs in China using the Gaussian two-step floating catchment area (G2SFCA) method. G2SFCA takes the EV charging stations with charging piles as supply and the EV ownership data as demand. The results indicate that (1) the eastern region of China has the highest density of EV charging stations (69.1%), followed by the central region, while the western region has the lowest density; (2) the spatial accessibility of EVCSs has a different pattern, where the central region has the highest accessibility, followed by the eastern and western regions; (3) the spatial mismatch between EVCSs and EV diffusion in the eastern region is larger than that of the other two regions, which may be attributed to the suboptimal layout of EV charging stations and the inconsistent pace between EV penetration and EV charging station construction; and (4) there is a significant spatial inequity in the accessibility of EVCSs across both all three regions and the entirety of China, with the western region exhibiting the highest inequity, followed by the central and eastern regions. Based on these findings, policy implications are drawn for different regions in China, which may aid policymakers in crafting strategic policies and subsidy programs to foster the advancement of EVCSs. Full article

(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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25 pages, 8987 KiB

Open AccessArticle

Landscape Sensitivity Assessment of Historic Districts Using a GIS-Based Method: A Case Study of Beishan Street in Hangzhou, China

Xueyan Yang

and

Jie Shen

ISPRS Int. J. Geo-Inf. 2023, 12(11), 462; https://doi.org/10.3390/ijgi12110462 - 12 Nov 2023

Viewed by 569

Abstract

Historic districts may be damaged during urban renewal. Landscape sensitivity can be used as a method to judge the ability of a landscape to resist change. This study proposes an improved method for assessing landscape sensitivity based on a geographic information system (GIS) according to the characteristics of historic districts. Based on a previous method, this study adds POI big data for comprehensive evaluation and uses objective criteria importance through intercriteria correlation (CRITIC) statistics instead of subjective methods to determine the weights. The assessment framework uses ecological, visual, and cultural sensitivity as primary criteria, which are further defined by several sub-criteria. The Beishan Street Historic District in Hangzhou, China, is used as a case study, and the results of the assessment are shown in the form of sensitivity maps. The results show that the maps can identify buildings in areas of high sensitivity and provide objective indicators for future conservation. Based on the sensitivity maps, this study innovatively used correlation analysis to reveal important interrelationships between ecological, visual, and cultural sensitivity. Assessment factors such as land use type need to be prioritized because they are more closely linked to other factors. Full article

(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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23 pages, 8496 KiB

Open AccessArticle

Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors

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ISPRS Int. J. Geo-Inf. 2023, 12(11), 457; https://doi.org/10.3390/ijgi12110457 - 08 Nov 2023

Viewed by 406

Abstract

Most commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By considering the sources of various scales and landmark datasets, as well as the spatial relationships between the selected objects and the detailed features of the entities, we propose an improved matching metric, the summation product of orientation and distance (SOD), combined with the shape descriptor based on feature point vectors, the shape area descriptor based on the minimum convex hull, and three other indicators, to establish multiple multi-scale road matching models. Through experiments, the comprehensive road matching model that combines SOD, orientation, distance and length is selected in this paper. When matching the road dataset with a scale of 1:50,000 and 1:10,000, the precision, recall, and F-score of the matching result of this model reached 97.31%, 94.33%, and 95.8%, respectively. In the case that the scale of the two datasets did not differ much, we concluded that the model can be used for matching between large-scale road datasets. Full article

(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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20 pages, 10561 KiB

Open AccessArticle

An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

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ISPRS Int. J. Geo-Inf. 2023, 12(11), 440; https://doi.org/10.3390/ijgi12110440 - 24 Oct 2023

Viewed by 731

Abstract

Augmented reality (AR) technology enables paper maps to dynamically express three-dimensional geographic information, realizing the fusion of virtual and real information. However, in the current mainstream AR development software, the virtual information usually consists of prefabricated components (prefabs), and the content creation for AR maps heavily relies on manual prefabrication. It leads to repetitive and error-prone prefabrication work, which restricts the design of the dynamic, interactive functions of AR maps. To solve this problem, this paper explored the possibility of automatically generating AR map prefabs using object detection models to establish a data conversion interface from paper maps to AR maps. First, we compared and analyzed various object detection models and selected YOLOv8x to recognize map point symbols. Then, we proposed a method to automatically generate AR map prefabs based on the predicted bounding boxes of the object detection model, which could generate prefabs with corresponding categories and positional information. Finally, we developed an AR map prototype system based on Android mobile devices. We designed an interaction method for information queries in the system to verify the effectiveness of the method proposed in this paper. The validation results indicate that our method can be practically applied to the AR map prefabrication process and can quickly generate AR map prefabs with high information accuracy. It alleviated the repetitive workload established through the manual prefabrication method and had specific feasibility and practicality. Moreover, it could provide solid data support for developing dynamic interactive functions of AR maps. Full article

(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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22 pages, 31161 KiB

Open AccessArticle

Using Dual Spatial Clustering Models for Urban Fringe Areas Extraction Based on Night-time Light Data: Comparison of NPP/VIIRS, Luojia 1-01, and NASA’s Black Marble

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ISPRS Int. J. Geo-Inf. 2023, 12(10), 408; https://doi.org/10.3390/ijgi12100408 - 04 Oct 2023

Viewed by 931

Abstract

Night-time light data (NTL) have been extensively utilized to map urban fringe areas, but to date, there has not been a comprehensive evaluation of the existing spatial clustering methods for delineating the urban fringe using different types of night-time light data. Therefore, we first selected three popular sources of night-time light data (i.e., NPP/VIIRS, Luojia 1-01, and NASA’s Black Marble) to identify the urban fringe. The recognition of spatial mutations across the urban–rural gradient was conducted based on changes in night light intensity using a spatial continuous wavelet transform model. Then, we employed three representative dual spatial clustering approaches (i.e., MK-Means, DBSC, and DSC) for extracting urban fringe areas using different NTL. By using dual spatial clustering, the spatial patterns of the mutation points were effectively transformed into homogeneous spatially adjacent clusters, enabling the measurement of similarity between mutation points. Taking Nanjing city, one of China’s megacities, as the study area, we found that (1) Compared with the fragmented and concentrated results obtained from the Luojia 1-01, NASA’s Black Marble and NPP/VIIRS data can effectively capture the abrupt change of urban fringes with NTL variations; (2) DSC provided a reliable approach for accurately extracting urban fringe areas using NASA’s Black Marble data. Full article

(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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25 pages, 6214 KiB

Open AccessArticle

Knowledge Graph Construction to Facilitate Indoor Fire Emergency Evacuation

Mingkang Da

Teng Zhong

and

Jiaqi Huang

ISPRS Int. J. Geo-Inf. 2023, 12(10), 403; https://doi.org/10.3390/ijgi12100403 - 03 Oct 2023

Viewed by 721

Abstract

Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan ‘5.25’ Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation. Full article

(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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20 pages, 4003 KiB

Open AccessArticle

Spatio-Temporal Relevance Classification from Geographic Texts Using Deep Learning

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ISPRS Int. J. Geo-Inf. 2023, 12(9), 359; https://doi.org/10.3390/ijgi12090359 - 01 Sep 2023

Viewed by 816

Abstract

The growing proliferation of geographic information presents a substantial challenge to the traditional framework of a geographic information analysis and service. The dynamic integration and representation of geographic knowledge, such as triples, with spatio-temporal information play a crucial role in constructing a comprehensive spatio-temporal knowledge graph and facilitating the effective utilization of spatio-temporal big data for knowledge-driven service applications. The existing knowledge graph (or geographic knowledge graph) takes spatio-temporal as the attribute of entity, ignoring the role of spatio-temporal information for accurate retrieval of entity objects and adaptive expression of entity objects. This study approaches the correlation between geographic knowledge and spatio-temporal information as a text classification problem, with the aim of addressing the challenge of establishing meaningful connections among spatio-temporal data using advanced deep learning techniques. Specifically, we leverage Wikipedia as a valuable data source for collecting and filtering geographic texts. The Open Information Extraction (OpenIE) tool is employed to extract triples from each sentence, followed by manual annotation of the sentences’ spatio-temporal relevance. This process leads to the formation of quadruples (time relevance/space relevance) or quintuples (spatio-temporal relevance). Subsequently, a comprehensive spatio-temporal classification dataset is constructed for experiment verification. Ten prominent deep learning text classification models are then utilized to conduct experiments covering various aspects of time, space, and spatio-temporal relationships. The experimental results demonstrate that the Bidirectional Encoder Representations from Transformer-Region-based Convolutional Neural Network (BERT-RCNN) model exhibits the highest performance among the evaluated models. Overall, this study establishes a foundation for future knowledge extraction endeavors. Full article

(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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22 pages, 6978 KiB

Open AccessArticle

Analysis of PM_2.5 Synergistic Governance Path from a Socio-Economic Perspective: A Case Study of Guangdong Province

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ISPRS Int. J. Geo-Inf. 2023, 12(8), 340; https://doi.org/10.3390/ijgi12080340 - 16 Aug 2023

Viewed by 602

Abstract

Analyzing the influencing factors of PM_2.5 concentration, scenario simulations, and countermeasure research to address the problem of PM_2.5 pollution in Guangdong Province is of great significance for governments at all levels for formulating relevant policies. In this study, the ChinaHighPM_2.5 dataset and economic and social statistics for Guangdong Province from 2010 to 2019 were selected, and a PM_2.5 pollution management compliance path formulation method based on the multi-scenario simulation was proposed by combining the differences in city types and PM_2.5 concentration prediction. Based on the prediction model of PM_2.5 concentration constructed by the Ridge and SVM models and facing the PM_2.5 pollution control target in 2025, the urban PM_2.5 pollution control scenario considering the characteristics of urban development was constructed. According to the scenario simulation results of the PM_2.5 prediction model, the PM_2.5 pollution control path suitable for Guangdong Province during the 14th Five-Year Plan period was explored. The coupling coordination model was used to explore the spatial and temporal pattern evolution of PM_2.5 pollution collaborative governance in various prefecture-level cities under the standard path, and the policy recommendations for PM_2.5 pollution control during the 14th Five-Year Plan period are proposed. The results showed the following: ① in the case of small samples, the model can provide effective simulation predictions for the study of urban pollutant management compliance pathways. ② Under the scenario of PM_2.5 management meeting the standard, in 2025, the annual average mass concentration of PM_2.5 in all prefecture-level cities in Guangdong Province will be lower than 22 μg/m³, and the annual average concentration of PM_2.5 in the whole province will drop from 25.91 μg/m³ to 21.04 μg/m³, which will fulfil the goal of reducing the annual average concentration of PM_2.5 in the whole province to below 22 μg/m³, as set out in the 14th Five-Year Plan for the Ecological Environmental Protection of Guangdong Province. ③ Under the path of PM_2.5 control and attainment, the regional coordination relationship among prefecture-level cities in Guangdong Province is gradually optimized, the number of intermediate-level coordinated cities will increase, and the overall spatial distribution pattern will be low in the middle and high in the surrounding area. Based on the characteristics of the four city types, it is recommended that a staggered development strategy be implemented to achieve synergy between economic development and environmental quality. Urban type I should focus on restructuring freight transportation to reduce urban pollutant emissions. City type II should focus on urban transportation and greening. For city type III, the focus should be on optimizing the industrial structure, adjusting the freight structure, and increasing the greening rate of the city. For city type IV, industrial upgrading, energy efficiency, freight structure, and management of industrial pollutant emissions should be strengthened. Full article

(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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22 pages, 3043 KiB

Open AccessArticle

PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting

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ISPRS Int. J. Geo-Inf. 2023, 12(6), 241; https://doi.org/10.3390/ijgi12060241 - 16 Jun 2023

Cited by 1 | Viewed by 1067

Abstract

Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules. Specifically, we use a new spatiotemporal attention multi-graph convolution that can extract extensive and comprehensive dynamic spatial dependence between nodes, in which multiple graph convolutions adopt progressive connections and spatiotemporal attention dynamically adjusts each item of the Chebyshev polynomial in graph convolutions. In addition, multi-scale time convolution was added to obtain an extensive and comprehensive dynamic time dependence from multiple receptive field features. We used real datasets to predict traffic speed and traffic flow, and the results were compared with a variety of typical prediction models. PMGCN has the smallest Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) results under different horizons (H = 15 min, 30 min, 60 min), which shows the superiority of the proposed model. Full article

(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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Review

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19 pages, 1271 KiB

Open AccessReview

Question Classification for Intelligent Question Answering: A Comprehensive Survey

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ISPRS Int. J. Geo-Inf. 2023, 12(10), 415; https://doi.org/10.3390/ijgi12100415 - 10 Oct 2023

Viewed by 817

Abstract

In the era of GeoAI, Geospatial Intelligent Question Answering (GeoIQA) represents the ultimate pursuit for everyone. Even generative AI systems like ChatGPT-4 struggle to handle complex GeoIQA. GeoIQA is domain complex IQA, which aims at understanding and answering questions accurately. The core of IQA is the Question Classification (QC), which mainly contains four types: content-based, template-based, calculation-based and method-based classification. These IQA_QC frameworks, however, struggle to be compatible and integrate with each other, which may be the bottleneck restricting the substantial improvement of IQA performance. To address this problem, this paper reviewed recent advances on IQA with the focus on solving question classification and proposed a comprehensive IQA_QC framework for understanding user query intention more accurately. By introducing the basic idea of the IQA mechanism, a three-level question classification framework consisting of essence, form and implementation is put forward which could cover the complexity and diversity of geographical questions. In addition, the proposed IQA_QC framework revealed that there are still significant deficiencies in the IQA evaluation metrics in the aspect of broader dimensions, which led to low answer performance, functional performance and systematic performance. Through the comparisons, we find that the proposed IQA_QC framework can fully integrate and surpass the existing classification. Although our proposed classification can be further expanded and improved, we firmly believe that this comprehensive IQA_QC framework can effectively help researchers in both semantic parsing and question querying processes. Furthermore, the IQA_QC framework can also provide a systematic question-and-answer pair/library categorization system for AIGCs, such as GPT-4. In conclusion, whether it is explicit GeoAI or implicit GeoAI, the IQA_QC can play a pioneering role in providing question-and-answer types in the future. Full article

(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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