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

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

Deadline for manuscript submissions: 20 March 2024 | Viewed by 2899

Special Issue Editors

Dr. Peng Peng

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; spatial and temporal information modelling; complex network analysis; maritime transportation; trajectory data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Shu Wang

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, China
Interests: geographical information science; geographical knowledge graph; geographical information retrieval; geoparsing; data & knowledge formalization
Dr. Maryam Lotfian

E-Mail Website
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

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, 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 (3 papers)

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Article
Spatio-Temporal Relevance Classification from Geographic Texts Using Deep Learning
by
Miao Tian
,
Xinxin Hu
,
Jiakai Huang
,
Kai Ma
,
Haiyan Li
,
Shuai Zheng
,
Liufeng Tao
and
Qinjun Qiu
ISPRS Int. J. Geo-Inf. 2023, 12(9), 359; https://doi.org/10.3390/ijgi12090359 - 01 Sep 2023
Viewed by 455
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 [...] Read more.
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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Article
Analysis of PM2.5 Synergistic Governance Path from a Socio-Economic Perspective: A Case Study of Guangdong Province
by
Kunkun Fan
,
Daichao Li
,
Cong Li
,
Xinlei Jin
,
Fei Ding
and
Zhan Zeng
ISPRS Int. J. Geo-Inf. 2023, 12(8), 340; https://doi.org/10.3390/ijgi12080340 - 16 Aug 2023
Viewed by 449
Abstract
Analyzing the influencing factors of PM2.5 concentration, scenario simulations, and countermeasure research to address the problem of PM2.5 pollution in Guangdong Province is of great significance for governments at all levels for formulating relevant policies. In this study, the ChinaHighPM2.5 [...] Read more.
Analyzing the influencing factors of PM2.5 concentration, scenario simulations, and countermeasure research to address the problem of PM2.5 pollution in Guangdong Province is of great significance for governments at all levels for formulating relevant policies. In this study, the ChinaHighPM2.5 dataset and economic and social statistics for Guangdong Province from 2010 to 2019 were selected, and a PM2.5 pollution management compliance path formulation method based on the multi-scenario simulation was proposed by combining the differences in city types and PM2.5 concentration prediction. Based on the prediction model of PM2.5 concentration constructed by the Ridge and SVM models and facing the PM2.5 pollution control target in 2025, the urban PM2.5 pollution control scenario considering the characteristics of urban development was constructed. According to the scenario simulation results of the PM2.5 prediction model, the PM2.5 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 PM2.5 pollution collaborative governance in various prefecture-level cities under the standard path, and the policy recommendations for PM2.5 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 PM2.5 management meeting the standard, in 2025, the annual average mass concentration of PM2.5 in all prefecture-level cities in Guangdong Province will be lower than 22 μg/m3, and the annual average concentration of PM2.5 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 PM2.5 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 PM2.5 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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Article
PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting
by
Zhenxin Li
,
Yong Han
,
Zhenyu Xu
,
Zhihao Zhang
,
Zhixian Sun
and
Ge Chen
ISPRS Int. J. Geo-Inf. 2023, 12(6), 241; https://doi.org/10.3390/ijgi12060241 - 16 Jun 2023
Cited by 1 | Viewed by 807
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 [...] Read more.
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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