Special Issue "Advanced Geo-Information Technologies for Anticipatory Computing"

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

Deadline for manuscript submissions: closed (30 March 2017).

Special Issue Editors

Guest Editor
Dr. Jason C. Hung Website E-Mail
Department of Information Technology, Overseas Chinese University, No:100, Chiao Kwang Rd., Taichung 407, Taiwan
Interests: mobile storage/server and mobile computing; multimedia presentation systems; software engineering and distributed software engineering; petri nets; intelligent agent; virtual university and distance learning; web document engineering; semantic web; ubiquitous computing; social computing
Guest Editor
Dr. Yu-Wei Chan Website E-Mail
Department of Computer Science and Information Management, Providence University, Taiwan
Interests: cognitive radio networks; game theoretic wireless networking; cloud computing; network economics
Guest Editor
Prof. Neil Yen Website E-Mail
Division of Computer Science, The University of Aizu, Aizu-Wakamatsu City, Fukushima-ken 965-8580, Japan
Interests: human-centered computing; computational intelligence
Guest Editor
Dr. Qingguo Zhou E-Mail
Distributed & Embedded System Lab, School of Information Science & Engineering, Lanzhou University, Lanzhou, China
Interests: emdedded sysytem; computational Intelligence

Special Issue Information

Dear Colleagues,

Recently, in big data technology, communication systems are implemented in such ways that data are collected from heterogeneous services and integrated seamlessly. To make big data technology more user-centric, further exploration is called for in the areas of data utilization and communication system in heterogeneous network environments. In addition, Internet of Things (IoT) is one of the important issues to describe several technologies and research disciplines that enable the IoT to reach out into the real world of physical objects. IoT is also a novel paradigm that is rapidly gaining in the scenario of wireless sensor networks and wireless telecommunications. The basic idea of this concept is the pervasive presence around our life style of a variety of things or objects. Cloud Computing provides a useful metaphor for combining capability at different scales. Such environments may therefore consist of devices ranging from handheld smart phones to supercomputers, to serve communities ranging from individuals to whole industries. The next computing environments will be provided the convergence computing infrastructure and theory by using big data processing scheme, Internet of things platform and cloud service architecture. Theoretical research contributions presenting new technologies, concepts, or analyses, reports on experiences and experiments of implementation and application of theories, and tutorials on new trends should be needed on IT research fields. For the aforementioned reasons, the special issue intends to give an overview of the state-of-the-art of issues and solution guidelines for the Advanced Geo-Information Technologies in Anticipatory Computing. In addition, it will provide completing the panorama of current research effort, which is widely inherent to topics of high interest for this Special Issue.

For this, this Special Issue of ISPRS, IJGI is addressing a call to researchers and academics for innovative papers submissions on the following (not limited) list of topics:

  • Convergence of theoretical model and application development based on Big Data, IoT and Cloud Computing
  • Interoperable service-oriented technologies to share real world data with Big Data, IoT and Cloud Computing
  • Interoperable middleware and architectures to communicate objects based on Big Data, IoT and Cloud Computing
  • Networking technologies for interconnect things with Big Data, IoT and Cloud Computing
  • Application services of Convergence with Big Data, IoT and Cloud Computing
  • Infrastructure for storage and computing capabilities for IoT application services and for processing big data on Cloud Service Architecture
  • Quality of service assurance for efficient resource management to allocate, track and resource utilization for IoT application services and for processing big data on Cloud Service Architecture.
  • High Performance/Parallel Computing for Big Data, IoT
  • Cloud/Grid/Stream Computing for Big Data and IoT
  • Applications and Security for Anticipatory Computing

Dr. Jason C. Hung
Dr. Yu-Wei Chan
Dr. Neil Y. Yen
Dr. Qingguo Zhou
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 papers will be 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 1000 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.

Published Papers (6 papers)

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Research

Open AccessArticle
Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area
ISPRS Int. J. Geo-Inf. 2017, 6(7), 228; https://doi.org/10.3390/ijgi6070228 - 23 Jul 2017
Cited by 7
Abstract
Landslide susceptibility mapping is the first and most important step involved in landslide hazard assessment. The purpose of the present study is to compare three nonlinear approaches for landslide susceptibility mapping and test whether coal mining has a significant impact on landslide occurrence [...] Read more.
Landslide susceptibility mapping is the first and most important step involved in landslide hazard assessment. The purpose of the present study is to compare three nonlinear approaches for landslide susceptibility mapping and test whether coal mining has a significant impact on landslide occurrence in coal mine areas. Landslide data collected by the Bureau of Land and Resources are represented by the X, Y coordinates of its central point; causative factors were calculated from topographic and geologic maps, as well as satellite imagery. The five-fold cross-validation method was adopted and the landslide/non-landslide datasets were randomly split into a ratio of 80:20. From this, five subsets for 20 times were acquired for training and validating models by GIS Geostatistical analysis methods, and all of the subsets were employed in a spatially balanced sample design. Three landslide models were built using support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models by selecting the median of the performance measures. Then, the three fitted models were compared using the area under the receiver operating characteristics (ROC) curves (AUC) and the performance measures. The results show that the prediction accuracies are between 73.43% and 87.45% in the training stage, and 67.16% to 73.13% in the validating stage for the three models. AUCs vary from 0.807 to 0.906 and 0.753 to 0.944 in the two stages, respectively. Additionally, three landslide susceptibility maps were obtained by classifying the range of landslide probabilities into four classes representing low (0–0.02), medium (0.02–0.1), high (0.1–0.85), and very high (0.85–1) probabilities of landslides. For the distributions of landslide and area percentages under different susceptibility standards, the SVM model has more relative balance in the four classes compared to the LR and the ANN models. The result reveals that the SVM model possesses better prediction efficiency than the other two models. Furthermore, the five factors, including lithology, distance from the road, slope angle, elevation, and land-use types, are the most suitable conditioning factors for landslide susceptibility mapping in the study area. The mining disturbance factor has little contribution to all models, because the mining method in this area is underground mining, so the mining depth is too deep to affect the stability of the slopes. Full article
(This article belongs to the Special Issue Advanced Geo-Information Technologies for Anticipatory Computing)
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Open AccessArticle
Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images
ISPRS Int. J. Geo-Inf. 2017, 6(6), 177; https://doi.org/10.3390/ijgi6060177 - 20 Jun 2017
Cited by 4
Abstract
Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is [...] Read more.
Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this paper, we propose a classification method named the multi-feature joint sparse algorithm (MF-SRU), in which spectral, topographic, and textural features are integrated as the decision-making features, and sparse representation of both center pixels and their eight neighborhood pixels is proposed to represent the spatial correlation of neighboring pixels, which can make good use of the spatial correlation of adjacent pixels. Experiments are performed on Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in Southeastern China, and the results show that the proposed method can effectively improve the extraction accuracy of mangroves. Full article
(This article belongs to the Special Issue Advanced Geo-Information Technologies for Anticipatory Computing)
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Open AccessArticle
A Formal Framework for Integrated Environment Modeling Systems
ISPRS Int. J. Geo-Inf. 2017, 6(2), 47; https://doi.org/10.3390/ijgi6020047 - 17 Feb 2017
Abstract
Integrated Environment Modeling (IEM) has become more and more important for environmental studies and applications. IEM systems have also been extended from scientific studies to much wider practical application situations. The quality and improved efficiency of IEM systems have therefore become increasingly critical. [...] Read more.
Integrated Environment Modeling (IEM) has become more and more important for environmental studies and applications. IEM systems have also been extended from scientific studies to much wider practical application situations. The quality and improved efficiency of IEM systems have therefore become increasingly critical. Although many advanced and creative technologies have been adopted to improve the quality of IEM systems, there is scarcely any formal method for evaluating and improving them. This paper is devoted to proposing a formal method to improve the quality and the developing efficiency of IEM systems. Two primary contributions are made. Firstly, a formal framework for IEM is proposed. The framework not only reflects the static and dynamic features of IEM but also covers different views from variant roles throughout the IEM lifecycle. Secondly, the formal operational semantics corresponding to the former model of the IEM is derived in detail; it can be used as the basis for aiding automated integrated modeling and verifying the integrated model. Full article
(This article belongs to the Special Issue Advanced Geo-Information Technologies for Anticipatory Computing)
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Open AccessArticle
Closing the Skill Gap of Cloud CRM Application Services in Cloud Computing for Evaluating Big Data Solutions
ISPRS Int. J. Geo-Inf. 2016, 5(12), 227; https://doi.org/10.3390/ijgi5120227 - 02 Dec 2016
Cited by 1
Abstract
Information systems (IS) continually motivate various improvements in the state-of-the-art of issues and solutions for advanced geo-information technologies in cloud computing. Reducing IS project risks and improving organizational performance has become an important issue. This study proposes a research framework, constructed from the [...] Read more.
Information systems (IS) continually motivate various improvements in the state-of-the-art of issues and solutions for advanced geo-information technologies in cloud computing. Reducing IS project risks and improving organizational performance has become an important issue. This study proposes a research framework, constructed from the Stimulus-Organism-Response (S-O-R) framework, in order to address the issues comprising the stimulus of project risk, the organism of project management, and the response of organizational performance for cloud service solutions. Cloud customer relationship management (cloud CRM) experts, based on cloud computing, with many years of project management experience, were selected for the interview sample in this study. Decision Making Trial and Evaluation Laboratory–based analytical network process (DEMATEL based-ANP, DANP) is a multiple-criteria decision-making (MCDM) analysis tool that does not have prior assumptions and it was used to experience the dynamic relationships among project risk, project management, and organizational performance. The study results include three directions: (a) Improving the internal business process performance can improve the efficiency of cloud CRM project processes and activities; (b) The emphasis on financial performance management can reduce the cost of a cloud CRM project so that the project can be completed within the approved budget; (c) Meeting user needs can improve user risk and reduce negative cloud CRM user experience. The scientific value of this study can be extended in order to study different projects, through research methods and frameworks, in order to explore project risk management and corporate performance improvements. Full article
(This article belongs to the Special Issue Advanced Geo-Information Technologies for Anticipatory Computing)
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Open AccessArticle
Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing
ISPRS Int. J. Geo-Inf. 2016, 5(11), 211; https://doi.org/10.3390/ijgi5110211 - 11 Nov 2016
Cited by 2
Abstract
In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to [...] Read more.
In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to efficiently disseminate spatial objects to mobile clients. In this paper, we consider a scenario in which spatial objects are broadcast to mobile clients over a wireless channel in a non-flat broadcast manner to process window queries. For such a scenario, we propose an efficient spatial air index method to handle window query access in non-flat wireless broadcast environments. The concept of largest empty rectangles is used to avoid unnecessary examination of the broadcast content, thus reducing the processing time for window queries. Simulation results show that the proposed spatial air index method outperforms the existing methods under various settings. Full article
(This article belongs to the Special Issue Advanced Geo-Information Technologies for Anticipatory Computing)
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Open AccessArticle
Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China
ISPRS Int. J. Geo-Inf. 2016, 5(10), 191; https://doi.org/10.3390/ijgi5100191 - 13 Oct 2016
Cited by 4
Abstract
In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection [...] Read more.
In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC) and landslide dot density (LDD) were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions) had a larger area under the ROC curve (0.7616) and a lower prediction error ratio (0.28%) than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km2) and the sum of two low susceptibility zone LDDs (0.82/100 km2) demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area. Full article
(This article belongs to the Special Issue Advanced Geo-Information Technologies for Anticipatory Computing)
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