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Electronics
Special Issues
Innovative System Architectures for High-Performance Geospatial Computing
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Innovative System Architectures for High-Performance Geospatial Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 2200

Special Issue Editors

Dr. Zhou Huang

E-Mail Website
Guest Editor
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China
Interests: geocomputation; high-performance computing; spatio-temporal data mining
Prof. Dr. Jiayuan Lin

E-Mail Website
Guest Editor
School of Geographical Sciences, Southwest University, Chongqing 400700, China
Interests: UAV environment remote sensing; geographic information system
Dr. Lin Wan

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: machine learning; high-performance computing

Special Issue Information

Dear Colleagues,

We are pleased to announce a CFP for a Special Issue, "Innovative System Architectures for High-Performance Geospatial Computing", in the Electronics Journal. This Special Issue invites submissions of original research and review articles related to high-performance geospatial computing, including—but not limited to—innovative hardware and software methods.

Geospatial computing is an emerging field that has become increasingly important in recent years, with applications ranging from navigation systems and smart transportation to environmental monitoring and disaster management. As such, the demand for high-performance geospatial computing systems has grown rapidly, creating a need for innovative system architectures that can support the processing and analysis of large-scale geospatial data.

This Special Issue aims to provide a platform for researchers and practitioners to share their latest findings and innovations in the field of high-performance geospatial computing. Topics of interest for this special issue include, but are not limited to:

  • Innovative hardware and software architectures for geospatial computing.
  • Algorithms and methods for high-performance geospatial data processing and analysis.
  • Cloud for geospatial computing and edge-based solutions.
  • AI for geospatial computing, new algorithms and methods.
  • Geospatial data visualization and user interfaces.
  • Applications of geospatial computing in navigation, transportation, environment, and disaster management.
  • Performance evaluation and benchmarking of geospatial computing systems.

We welcome submissions of original research articles, reviews, and short communications. All submissions will be peer-reviewed by experts in the field and accepted papers will be published in the Special Issue.

Dr. Zhou Huang
Prof. Dr. Jiayuan Lin
Dr. Lin Wan
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • high-performance geospatial computing
  • large-scale geospatial data
  • geospatial data visualization
  • geospatial computing systems

Published Papers (2 papers)

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Research

24 pages, 9277 KiB  
Article
MFM: A Multiple-Features Model for Leisure Event Recommendation in Geotagged Social Networks
by Yazhao Wu, Xia Peng, Yueyan Niu and Zhiming Gui
Electronics 2024, 13(1), 112; https://doi.org/10.3390/electronics13010112 - 27 Dec 2023
Viewed by 738
Abstract
Event-based social networks (EBSNs) are rich in information about users and leisure events. The willingness of users to participate in leisure events is influenced by many factors such as event time, location, content, organizer, and social relationship factors of users. Event recommendation systems [...] Read more.
Event-based social networks (EBSNs) are rich in information about users and leisure events. The willingness of users to participate in leisure events is influenced by many factors such as event time, location, content, organizer, and social relationship factors of users. Event recommendation systems in EBSNs can help leisure event organizers to accurately find users who want to participate in events. However, to address the existing cold-start problems and improve the accuracy of event recommendations, we propose a multiple-feature-based leisure event recommendation model (MFM). We introduce the user’s social contacts into the user preference features and construct a user feature space by integrating the features of the user preferences for events and organizers and preferences of the user’s closest friends. Moreover, considering the behavioral differences between active and inactive users, we extracted the respective features and trained the feature weight models. Finally, the experimental results showed that in comparison with the baseline models, the precision of the MFM is higher by at least 7.9%. Full article
(This article belongs to the Special Issue Innovative System Architectures for High-Performance Geospatial Computing)
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12 pages, 2297 KiB  
Article
Research on a High-Performance Rock Image Classification Method
by Mingshuo Ma, Zhiming Gui and Zhenji Gao
Electronics 2023, 12(23), 4805; https://doi.org/10.3390/electronics12234805 - 28 Nov 2023
Cited by 1 | Viewed by 861
Abstract
Efficient and convenient rock image classification methods are important for geological research. They help in identifying and categorizing rocks based on their physical and chemical properties, which can provide insights into their geological history, origin, and potential uses in various applications. The classification [...] Read more.
Efficient and convenient rock image classification methods are important for geological research. They help in identifying and categorizing rocks based on their physical and chemical properties, which can provide insights into their geological history, origin, and potential uses in various applications. The classification and identification of rocks often rely on experienced and knowledgeable professionals and are less efficient. Fine-grained rock image classification is a challenging task because of the inherent subtle differences between highly confusing categories, which require a large number of data samples and computational resources, resulting in low recognition accuracy, and are difficult to apply in mobile scenarios, requiring the design of a high-performance image processing classification architecture. In this paper we design a knowledge distillation and high-accuracy feature localization comparison network (FPCN)-based learning architecture for generating small high-performance rock image classification models. Specifically, for a pair of images, we interact with the feature vectors generated from the localized feature maps to capture common and unique features, let the network focus on more complementary information according to the different scales of the objects, and then the important features of the images learned in this way are made available for the micro-model to learn the critical information for discrimination via model distillation. The proposed method improves the accuracy of the micro-model by 3%. Full article
(This article belongs to the Special Issue Innovative System Architectures for High-Performance Geospatial Computing)
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