Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.8
Journals
IJGI
Special Issues
The Application of AI Techniques on Geo-Information Systems
share announcement

The Application of AI Techniques on Geo-Information Systems

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 9264

Special Issue Editors

Prof. Dr. José F. Vicent

E-Mail Website1 Website2
Guest Editor
Department of Computer Science and Artificial Intelligence, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig-Alicante, Spain
Interests: complex networks; machine learning; spatial networks; multilayer networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Leandro Tortosa

E-Mail Website
Co-Guest Editor
Department of computer Science and Artificial Intelligence, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig-Alicante, Spain
Interests: complex networks; urban networks; multilayer networks; spatial networks
Special Issues, Collections and Topics in MDPI journals
Dr. Manuel Curado

E-Mail Website
Co-Guest Editor
Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente del Raspeig, Ap. Correos 99, E-03080 Alicante, Spain
Interests: complex networks; machine learning; spatial networks; multilayer networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to collecting original research contributions focused on the application of artificial intelligence (AI) methods for acquisition, filtering, management, analysis, discovery, and visualization of geo-information systems from multiple sources. We aim to take an integrated approach to AI solutions that fosters a robust collaboration between the computer science, geo-information science, and machine learning communities.

This field applies many techniques of the most general AI, such as machine learning, deep learning, semantic representation and analysis, knowledge discovery, data mining, and soft computing. However, the specificities and importance of the geospatial dimension, its heterogeneity, the need for representing distinct semantics of locations, as well as analyzing the role of their temporal changes by applying geospatial and temporal reasoning pose new challenges and opportunities faced by AI.

Over the last few years, AI and deep learning methods in particular, have had a transformative impact in fields such as natural language processing or computer vision, significantly advancing the state-of-the-art solutions to problems like parsing natural language, classifying unstructured data, or semantically segmenting contents. These same techniques can also empower a next generation of geo-information systems, providing the ability to combine spatial analysis with location-based discovery and analysis of relevant information.

This Special Issue aims at collecting original and high quality papers within the research field of AI techniques for geo-information systems. As such, we invite the community of professionals and researchers to submit their work within this field of interest.

The list of topics includes, but is not limited to:

Big data infrastructures for geo-information systems
Sensor network data acquisition, processing, and analytics
Interactive applications
Spatial networks systems
Big data in cultural heritage
Artificial intelligence technologies and smart cities
Trajectory and movement analysis
Acquisition, management, and classification of data
Applications from location-based data
Big data in smart accessibility tourism

Dr. José F. Vicent
Dr. Leandro Tortosa
Dr. Manuel Curado
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

  • big data
  • spatial networks
  • smart cities
  • geo-located data
  • complex networks

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

28 pages, 13202 KiB  
Article
The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity
by Álvaro Bernabeu-Bautista, Leticia Serrano-Estrada, V. Raul Perez-Sanchez and Pablo Martí
ISPRS Int. J. Geo-Inf. 2021, 10(11), 747; https://doi.org/10.3390/ijgi10110747 - 3 Nov 2021
Cited by 12 | Viewed by 3177
Abstract
This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that purpose, a comparison is made between location patterns of geolocated data from various [...] Read more.
This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that purpose, a comparison is made between location patterns of geolocated data from various social networks (i.e., Google Places, Foursquare, Twitter, Airbnb and Idealista) and statistical information such as land value, average gross income, and population distribution by age range. The main findings show that there is no direct relationship between land value or age of registered population and the amount of social network data generated in a given area. However, a noteworthy coincidence was observed between Google Places data-clustering patterns, which represent the offer of economic activities, and the spatial concentration of the other LBSNs analyzed, suggesting that data from these sources are mostly generated in areas with a high density of economic activities. Full article
(This article belongs to the Special Issue The Application of AI Techniques on Geo-Information Systems)
►▼ Show Figures

Figure 1

15 pages, 1694 KiB  
Article
A New Methodology to Study Street Accessibility: A Case Study of Avila (Spain)
by Manuel Curado, Rocio Rodriguez, Manuel Jimenez, Leandro Tortosa and Jose F. Vicent
ISPRS Int. J. Geo-Inf. 2021, 10(7), 491; https://doi.org/10.3390/ijgi10070491 - 20 Jul 2021
Cited by 2 | Viewed by 2189
Abstract
Taking into account that accessibility is one of the most strategic and determining factors in economic models and that accessibility and tourism affect each other, we can say that the study and improvement of one of them involved the development of the other. [...] Read more.
Taking into account that accessibility is one of the most strategic and determining factors in economic models and that accessibility and tourism affect each other, we can say that the study and improvement of one of them involved the development of the other. Using network analysis, this study presents an algorithm for labeling the difficulty of the streets of a city using different accessibility parameters. We combine network structure and accessibility factors to explore the association between innovative behavior within the street network, and the relationships with the commercial activity in a city. Finally, we present a case study of the city of Avila, locating the most inaccessible areas of the city using centrality measures and analyzing the effects, in terms of accessibility, on the commerce and services of the city. Full article
(This article belongs to the Special Issue The Application of AI Techniques on Geo-Information Systems)
►▼ Show Figures

Figure 1

27 pages, 3912 KiB  
Article
A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow
by Yong Han, Tongxin Peng, Cheng Wang, Zhihao Zhang and Ge Chen
ISPRS Int. J. Geo-Inf. 2021, 10(4), 222; https://doi.org/10.3390/ijgi10040222 - 3 Apr 2021
Cited by 15 | Viewed by 2982
Abstract
Accurate prediction of citywide short-term metro passenger flow is essential to urban management and transport scheduling. Recently, an increasing number of researchers have applied deep learning models to passenger flow prediction. Nevertheless, the task is still challenging due to the complex spatial dependency [...] Read more.
Accurate prediction of citywide short-term metro passenger flow is essential to urban management and transport scheduling. Recently, an increasing number of researchers have applied deep learning models to passenger flow prediction. Nevertheless, the task is still challenging due to the complex spatial dependency on the metro network and the time-varying traffic patterns. Therefore, we propose a novel deep learning architecture combining graph attention networks (GAT) with long short-term memory (LSTM) networks, which is called the hybrid GLM (hybrid GAT and LSTM Model). The proposed model captures the spatial dependency via the graph attention layers and learns the temporal dependency via the LSTM layers. Moreover, some external factors are embedded. We tested the hybrid GLM by predicting the metro passenger flow in Shanghai, China. The results are compared with the forecasts from some typical data-driven models. The hybrid GLM gets the smallest root-mean-square error (RMSE) and mean absolute percentage error (MAPE) in different time intervals (TIs), which exhibits the superiority of the proposed model. In particular, in the TI 10 min, the hybrid GLM brings about 6–30% extra improvements in terms of RMSE. We additionally explore the sensitivity of the model to its parameters, which will aid the application of this model. Full article
(This article belongs to the Special Issue The Application of AI Techniques on Geo-Information Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop