Special Issue "GIS Software and Engineering for Big Data"

Special Issue Editors

Prof. Dr. Peng Yue
Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei, 430079, China
Interests: Earth science data and information systems, GIS, Data science, Semantics, Cloud computing
Special Issues and Collections in MDPI journals
Prof. Dr. Danielle Ziebelin
Website
Guest Editor
STEAMER group, LIG, Universite Grenoble Alpes - UGA, LIG - Bâtiment IMAG - CS 40700 - 38058 GRENOBLE CEDEX, France
Interests: GIS, Knowledge representation and reasoning, Problem solving systems, Semantic web, Ontologies, Spatio-temporal reasoning, Data integration
Dr. Yaxing Wei
Website
Guest Editor
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, P.O. Box 2008, MS - 6290 Oak Ridge, TN 37831 – 6290, USA
Interests: Geospatial data management and systems, Geospatial standards and interoperability; Web GIS, Geospatial information analysis, Geospatial services

Special Issue Information

Dear Colleagues,

The increasing spread and usage of big data is changing the way data are managed and analyzed. The capabilities of traditional GIS (geographical information system) software are often limited in dealing with big data challenges, such as versatile data forms, steaming processing, large scale parallel computing, and dynamic mapping and visualization. Significant improvements are needed in innovative software development and engineering applications of GIS. First, GIS needs to be extended to accommodate dynamic observations of sensors including volunteered geographic information (VGI). Second, new data models and indexing algorithms are needed to store and access unstructured, multidimensional, and dynamic data. Third, the computing paradigm calls for innovation to meet the demands of stream processing, real-time analysis, and information extraction from large-scale datasets. Fourth, novel methods in mapping and visualization shall be studied to dynamically display, analyze, and simulate geographical phenomena and their progresses. Finally, data mining and analysis technologies for big geospatial data deserve further research to perform data, information, and knowledge transformations.

As a result, the GIS software and engineering domain has seen increasing applications for advanced information technologies, such as the map/reduce computing paradigm, stream processing, NoSQL/NewSQL, block chain, and artificial intelligence technologies. This Special Issue intends to collect the latest and future directions in GIS software development and engineering applications to deal with spatio-temporal big data. We invite authors to submit their original papers. Potential topics include, but are not limited to:

  • Data and computational architecture of GIS
  • Internet of Things and sensor observations in GIS
  • High-performance geo-computation and geo-stream processing
  • Geospatial data model and data cube
  • Workflow and provenance
  • Distributed and scalable geospatial database
  • Web GIS and geospatial services
  • Virtual reality (VR) and augmented reality(AR) GIS
  • Spatio-temporal big data visualization
  • Knowledge representation in GIS
  • Artificial intelligence in GIS
  • Block chain for GIS
  • GIS tools and applications for big data

Prof. Dr. Peng Yue
Prof. Dr. Danielle Ziebelin
Dr. Yaxing Wei
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.

Keywords

  • Software architecture
  • Computational architecture
  • Distributed geoprocessing
  • Parallel geo-computation
  • Geospatial database
  • AR/VR GIS
  • Cloud GIS
  • Geospatial artificial intelligence
  • Geospatial block chain
  • Big data GIS applications

Published Papers (5 papers)

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Open AccessArticle
An Illumination Insensitive Descriptor Combining the CSLBP Features for Street View Images in Augmented Reality: Experimental Studies
ISPRS Int. J. Geo-Inf. 2020, 9(6), 362; https://doi.org/10.3390/ijgi9060362 - 01 Jun 2020
Abstract
The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric [...] Read more.
The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric local binary pattern (CS-LBP) into a common feature description framework. This proposed descriptor can be used to improve the performance of eight commonly used feature-matching algorithms, e.g., SIFT, SURF, DAISY, BRISK, ORB, FREAK, KAZE, and AKAZE. We perform the experiments on five street view image sequences with different illumination changes. By comparing with the performance of eight original algorithms, the evaluation results show that our improved algorithms can improve the matching accuracy of street view images with changing illumination. Further, the time consumption only increases a little. Therefore, our combined descriptors are much more robust against light changes to satisfy the high precision requirement of augmented reality (AR) system. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Open AccessArticle
Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows
ISPRS Int. J. Geo-Inf. 2020, 9(2), 119; https://doi.org/10.3390/ijgi9020119 - 21 Feb 2020
Cited by 2
Abstract
AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing [...] Read more.
AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Open AccessArticle
A Universal Generating Algorithm of the Polyhedral Discrete Grid Based on Unit Duplication
ISPRS Int. J. Geo-Inf. 2019, 8(3), 146; https://doi.org/10.3390/ijgi8030146 - 19 Mar 2019
Cited by 1
Abstract
Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region [...] Read more.
Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region restriction”. First, the grid coordinate system and the corresponding spatial rectangular coordinate system are established to determine the rectangular coordinates of any grid cell node. Then, the type of the subdivision grid system to be calculated is determined to identify the three key factors affecting the grid types, which are the position of the starting point, the length of the starting edge, and the direction of the starting edge. On this basis, the effective boundary of a multiscale grid can be determined and the grid coordinates of a multiscale grid can be obtained. A one-to-one correspondence between the multiscale grids and subdivision types can be established. Through the appropriate rotation, translation and scaling of the multiscale grid, the node coordinates of a single triangular grid system are calculated, and the relationships between the nodes of different levels are established. Finally, this paper takes a hexagonal grid as an example to carry out the experiment verifications by converting a single triangular grid system (plane) directly to a single triangular grid with a positive icosahedral surface to generate a positive icosahedral surface grid. The experimental results show that the algorithm has good universality and can generate the multiscale grid of an arbitrary grid configuration by adjusting the corresponding starting transformation parameters. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Open AccessArticle
Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data
ISPRS Int. J. Geo-Inf. 2019, 8(1), 21; https://doi.org/10.3390/ijgi8010021 - 10 Jan 2019
Cited by 2
Abstract
Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of [...] Read more.
Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided (http://www.higis.org.cn:8080/hibo). Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Open AccessTechnical Note
Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm
ISPRS Int. J. Geo-Inf. 2020, 9(2), 136; https://doi.org/10.3390/ijgi9020136 - 24 Feb 2020
Abstract
The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile [...] Read more.
The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile devices and the development of network facilities, social media has attracted widespread attention as a new source of disaster data. The characteristics of rich disaster information, near real-time transmission channels, and low-cost data production have been favored by many researchers. These researchers have used different methods to study disaster reduction based on the different dimensions of information contained in social media, including time, location and content. However, current research is not sufficient and rarely combines specific road condition information with public emotional information to detect traffic impact areas and assess the spatiotemporal influence of these areas. Thus, in this paper, we used various methods, including natural language processing and deep learning, to extract the fine-grained road condition information and public emotional information contained in social media text to comprehensively detect and analyze traffic impact areas during a rainstorm disaster. Furthermore, we proposed a model to evaluate the spatiotemporal influence of these detected traffic impact areas. The heavy rainstorm event in Beijing, China, in 2018 was selected as a case study to verify the validity of the disaster reduction method proposed in this paper. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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