Special Issue "Spatial Data Infrastructures for Big Geospatial Sensing Data"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 30 May 2022.

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, Collections and Topics in MDPI journals
Dr. Ingo Simonis
E-Mail Website
Guest Editor
Open Geospatial Consortium (OGC), Rockville, MD 20852-3149, USA
Interests: software system architecture; distributed computing; semantic web; geospatial analytics; geospatial AI
Prof. Dr. Maged N. Kamel Boulos
E-Mail Website
Guest Editor
Professor of Department of Digital Health Systems, Sun Yat-sen University, Guangzhou 510000, China
Interests: health GIS; VR/ARGIS; geospatial blockchain; semantic web; social web
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decades, information infrastructure, such as spatial data infrastructure (SDI), e-science, and cyberinfrastructure, have been benefiting scientific communities and supporting scientific research. The infrastructure could be used to exploit big data. Many national and international infrastructure projects have been conducted or are on-going, including Global Earth Observation System of Systems (GEOSS), the European Commission’s INSPIRE, and the U.S. NSF EarthCube. There are also many well-known groups working on geospatial infrastructure research and projects, including the intergovernmental Group on Earth Observations (GEO), Committee on Earth Observation Satellites (CEOS), United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM), IEEE GRSS Earth Science Informatics Technical Committee (ESI TC), U.S. Federation of Earth Science Information Partners (ESIP), and major space agencies around the world, including DLR, ESA, JAXA, NASA, NOAA, and USGS. The major international bodies setting standards for SDI, such as the Open Geospatial Consortium (OGC) and ISO TC 211, have over 400 organizational members and 40 country members, respectively.

Big geospatial data could take infrastructure-based approaches. On the one hand, large amounts of existing Earth science data are collected by remote sensors, taking advantage of the Internet of Things (IoT) and sensor web technologies. On the other hand, volunteered geographic information (VGI) and citizen sensors are bringing social content to geospatial data. Both could complement each other for enhanced data analysis and scientific discovery. These data, collectively annotated as big geospatial sensing data, can be acquired, visualized, analyzed, and shared through a computational cyberinfrastructure using open, extensible, and interoperable computational software. This Special Issue intends to collect current developments and future directions of using infrastructural methods, tools, and technologies to support big geospatial sensing data. We invite authors to submit their original papers. Potential topics include, but are not limited to:

  • Data and information policies for spatial data infrastructure
  • Data stewardship and preservation
  • Provenance and quality
  • Knowledge representation and information models
  • Cyberinfrastructure, interoperability and standardization
  • Data discovery and access
  • Web-based services and analysis for big geospatial sensing data
  • Semantic representation of the spatial and temporal relationships between entities in the geosciences (e.g., spatial and process ontologies, vocabularies, semantic web)
  • Sensor web and applications
  • Cloud computing
  • Geospatial information, knowledge, and decision support systems
  • Tools supporting spatial and temporal analyses of g geospatial sensing data and their applications
  • Emerging information technologies and their applications in big geospatial sensing data

Prof. Dr. Peng Yue
Dr. Ingo Simonis
Prof. Dr. Maged N. Kamel Boulos
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. Remote Sensing 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

  • Spatial Data Infrastructure (SDI)
  • Cyberinfrastructure
  • Geospatial Standards
  • Big Geospatial Data
  • Distributed Geospatial Data Management
  • Geospatial Interoperability and Semantics
  • Cloud Computing
  • Big Geospatial Data Sensing
  • Big Sensing Data Analytics
  • Big Geospatial Data Processing

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion
Remote Sens. 2019, 11(24), 2898; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242898 - 05 Dec 2019
Cited by 13 | Viewed by 1583
Abstract
Earth observation data with high spatiotemporal resolution are critical for dynamic monitoring and prediction in geoscience applications, however, due to some technique and budget limitations, it is not easy to acquire satellite images with both high spatial and high temporal resolutions. Spatiotemporal image [...] Read more.
Earth observation data with high spatiotemporal resolution are critical for dynamic monitoring and prediction in geoscience applications, however, due to some technique and budget limitations, it is not easy to acquire satellite images with both high spatial and high temporal resolutions. Spatiotemporal image fusion techniques provide a feasible and economical solution for generating dense-time data with high spatial resolution, pushing the limits of current satellite observation systems. Among existing various fusion algorithms, deeplearningbased models reveal a promising prospect with higher accuracy and robustness. This paper refined and improved the existing deep convolutional spatiotemporal fusion network (DCSTFN) to further boost model prediction accuracy and enhance image quality. The contributions of this paper are twofold. First, the fusion result is improved considerably with brand-new network architecture and a novel compound loss function. Experiments conducted in two different areas demonstrate these improvements by comparing them with existing algorithms. The enhanced DCSTFN model shows superior performance with higher accuracy, vision quality, and robustness. Second, the advantages and disadvantages of existing deeplearningbased spatiotemporal fusion models are comparatively discussed and a network design guide for spatiotemporal fusion is provided as a reference for future research. Those comparisons and guidelines are summarized based on numbers of actual experiments and have promising potentials to be applied for other image sources with customized spatiotemporal fusion networks. Full article
(This article belongs to the Special Issue Spatial Data Infrastructures for Big Geospatial Sensing Data)
Show Figures

Graphical abstract

Review

Jump to: Research

Review
An Overview of Platforms for Big Earth Observation Data Management and Analysis
Remote Sens. 2020, 12(8), 1253; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081253 - 16 Apr 2020
Cited by 32 | Viewed by 3734
Abstract
In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. [...] Read more.
In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. To meet these demands, novel technologies have been proposed and developed, based on cloud computing and distributed systems, such as array database systems, MapReduce systems and web services to access and process big Earth observation data. Currently, these technologies have been integrated into cutting edge platforms in order to support a new generation of SDI for big Earth observation data. This paper presents an overview of seven platforms for big Earth observation data management and analysis—Google Earth Engine (GEE), Sentinel Hub, Open Data Cube (ODC), System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), openEO, JEODPP, and pipsCloud. We also provide a comparison of these platforms according to criteria that represent capabilities of the EO community interest. Full article
(This article belongs to the Special Issue Spatial Data Infrastructures for Big Geospatial Sensing Data)
Show Figures

Graphical abstract

Back to TopTop