Special Issue "GIS Software and Engineering for Big Data"

Special Issue Editors

Prof. Dr. Peng Yue
E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 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
E-Mail 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
E-Mail 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 1400 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 (9 papers)

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Article
The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding
ISPRS Int. J. Geo-Inf. 2021, 10(9), 572; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10090572 - 24 Aug 2021
Viewed by 466
Abstract
Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. [...] Read more.
Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advances in deep learning have promoted the use of the neural network to improve the performance of geocoding. However, most of the existing approaches consider only the local context, e.g., neighboring words in a sentence, as opposed to the global context, e.g., the topic of the document. Lack of global information may have a severe impact on the robustness of the model. To fill the research gap, this paper proposes a novel global context embedding approach to generate linguistic and geospatial features through topic embedding and location embedding, respectively. A deep neural network called LGGeoCoder, which integrates local and global features, is developed to solve the geocoding as a classification problem. The experiments on a Wikipedia place name dataset demonstrate that LGGeoCoder achieves competitive performance compared with state-of-the-art models. Furthermore, the effect of introducing global linguistic and geospatial features in geocoding to alleviate the ambiguity and scarcity problem is discussed. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Article
A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions
ISPRS Int. J. Geo-Inf. 2021, 10(2), 87; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020087 - 19 Feb 2021
Viewed by 750
Abstract
Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. [...] Read more.
Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called “Racines”, gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Article
A Blockchain Solution for Securing Real Property Transactions: A Case Study for Serbia
ISPRS Int. J. Geo-Inf. 2021, 10(1), 35; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010035 - 15 Jan 2021
Cited by 2 | Viewed by 1072
Abstract
The origins of digital money and blockchain technology goes back to the 1980s, but in the last decade, the blockchain technology gained large popularity in the financial sector with the appearance of cryptocurrencies such as Bitcoin. However, recently, many other fields of application [...] Read more.
The origins of digital money and blockchain technology goes back to the 1980s, but in the last decade, the blockchain technology gained large popularity in the financial sector with the appearance of cryptocurrencies such as Bitcoin. However, recently, many other fields of application have been recognized, particularly with the development of smart contracts. Among them is the possible application of blockchain technology in the domain of land administration, mostly as a tool for transparency in the developing countries and means to fight corruption. However, developed countries also find interest in launching pilot projects to test their applicability in land administration domain for reasons such as to increase the speed and reduce costs of the real property transactions through a more secure environment. In this paper, we analyse how transactions are handled in Serbian land administration and how this process may be supported by modern ledger technologies such as blockchain. In order to analyse how blockchain could be implemented to support transactions in land information systems (LIS), it is necessary to understand cadastral processes and transactions in LIS, as well as legislative and organizational aspects of LIS. Transactions in cadastre comprise many actors and utilize both alphanumeric (descriptive or legal) data and geospatial data about property boundaries on the cadastral map. Based on the determined requirements for the blockchain-based LIS, we propose a system architecture for its implementation. Such a system keeps track of transactions in LIS in an immutable and tamper-proof manner to increase the security of the system and consequently increase the speed of transactions, efficiency, and data integrity without a significant impact on the existing laws and regulations. The system is anticipated as a permissioned public blockchain implemented on top of the Ethereum network. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Article
An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase
ISPRS Int. J. Geo-Inf. 2020, 9(11), 625; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110625 - 25 Oct 2020
Cited by 1 | Viewed by 802
Abstract
Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their [...] Read more.
Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Article
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://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060362 - 01 Jun 2020
Viewed by 731
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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Article
Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows
ISPRS Int. J. Geo-Inf. 2020, 9(2), 119; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020119 - 21 Feb 2020
Cited by 3 | Viewed by 1498
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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Article
A Universal Generating Algorithm of the Polyhedral Discrete Grid Based on Unit Duplication
ISPRS Int. J. Geo-Inf. 2019, 8(3), 146; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8030146 - 19 Mar 2019
Cited by 1 | Viewed by 1408
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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Article
Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data
ISPRS Int. J. Geo-Inf. 2019, 8(1), 21; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010021 - 10 Jan 2019
Cited by 6 | Viewed by 1441
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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Technical 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://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020136 - 24 Feb 2020
Viewed by 1351
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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