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Spatial Data Infrastructures for Big Geospatial Sensing Data

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

Deadline for manuscript submissions: closed (30 May 2022) | Viewed by 31768

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Special Issue Editors

Prof. Dr. Peng Yue

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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 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. 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 2700 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 (4 papers)

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Research

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19 pages, 4741 KiB

Open AccessArticle

A Serverless-Based, On-the-Fly Computing Framework for Remote Sensing Image Collection

by Jin Wu, Mingbo Wu, Haiyan Li, Lijuan Li and Leilei Li

Remote Sens. 2022, 14(7), 1728; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071728 - 03 Apr 2022

Cited by 4 | Viewed by 2233

Abstract

The rapid growth of remote sensing data calls for the construction of new computational models for algorithmic exploration, which requires on-demand execution, instant response, and multitenancy. We call this model on-the-fly computing, which could reduce the complexity of cloud programming for remote sensing data analysis and benefit from efficient multiplexing. As an advancement of cloud computing, serverless computing makes it possible to realize the on-the-fly computational model. In the study, the concise definition of an on-the-fly computing model for remote sensing data analysis and the corresponding software architecture based on the serverless computing commodities are presented. The proof-of-concept experiments have suggested that the on-the-fly computing model for remote sensing data analysis can be efficiently implemented as a serverless software. The response time is mainly related to the tile reading operation and data structure conversion. In the case of high concurrency, the system can scale to hundreds of instances in seconds. Full article

(This article belongs to the Special Issue Spatial Data Infrastructures for Big Geospatial Sensing Data)

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24 pages, 3365 KiB

Open AccessArticle

An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion

by Zhenyu Tan, Liping Di, Mingda Zhang, Liying Guo and Meiling Gao

Remote Sens. 2019, 11(24), 2898; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242898 - 05 Dec 2019

Cited by 75 | Viewed by 8520

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 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)

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Review

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14 pages, 1359 KiB

Open AccessReview

Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health

by Keumseok Koh, Ayaz Hyder, Yogita Karale and Maged N. Kamel Boulos

Remote Sens. 2022, 14(13), 2996; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14132996 - 23 Jun 2022

Cited by 4 | Viewed by 3365

Abstract

Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms ‘big’ geospatial data and geospatial ‘big data’ have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies. Full article

(This article belongs to the Special Issue Spatial Data Infrastructures for Big Geospatial Sensing Data)

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25 pages, 1451 KiB

Open AccessEditor’s ChoiceReview

An Overview of Platforms for Big Earth Observation Data Management and Analysis

by Vitor C. F. Gomes, Gilberto R. Queiroz and Karine R. Ferreira

Remote Sens. 2020, 12(8), 1253; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081253 - 16 Apr 2020

Cited by 158 | Viewed by 15840

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. 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)

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