Managing Big, Linked, and Open Earth-Observation Data: Using the TELEIOS\/LEO software stack

Koubarakis, Manolis; Kyzirakos, Kostis; Nikolaou, Charalampos; Garbis, George; Bereta, Konstantina; Dogani, Roi; Giannakopoulou, Stella; Smeros, Panayiotis; Savva, Dimitrianos; Stamoulis, George; Vlachopoulos, Giannis; Manegold, Stefan; Kontoes, Charalampos; Herekakis, Themistocles; Papoutsis, Ioannis; Michail, Dimitrios

doi:10.1109/mgrs.2016.2530410

Cited by 37 publications

(13 citation statements)

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“…In this paper we have seen that GeoTriples and TripleGeo are both state-of-the-art tools for transforming geospatial data into RDF. However, transforming geospatial data from their native formats into RDF is only one phase of the life-cycle of linked geospatial data which we have presented in [31]. The requirements of this life-cycle are addressed by the linked geospatial data tool suite that has been developed by our group at the National and Kapodistrian University of Athens and has been recently surveyed in [1].…”

Section: Discussionmentioning

confidence: 99%

“…The latter could be either the primary key of the table or a row number of each tuple in the dBase table of a shapefile. 31 Combined with a URI template, the unique identifier is used to produce the URIs that are the subjects of the produced triples. For each column of the input data source, GeoTriples generates an RDF predicate according to the name of the column and a predicate object map.…”

Section: Automatic Generation Of R2rml and Rml Mappingsmentioning

confidence: 99%

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GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

Kyzirakos

Savva

Vlachopoulos

et al. 2018

Journal of Web Semantics

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A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop.

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Section: Discussionmentioning

confidence: 99%

Section: Automatic Generation Of R2rml and Rml Mappingsmentioning

confidence: 99%

GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

Kyzirakos

Savva

Vlachopoulos

et al. 2018

Journal of Web Semantics

Self Cite

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“…With increasing spatial resolutions of sensors and shorter revisiting times, a call for 'bring the user to the data and not the data to the user' has started in the EO community [29]. Now, more and more EO data that are made freely available by space agencies come from various archives [30]. From digital Earth to big Earth data, all EO data play an important role and will provide a new vision and methodology to Earth sciences [7].…”

Section: Big Data and Beodmentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

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“…On the other hand, the geospatial semantic web may also be a solution for dealing with the challenge of heterogeneity in big spatial data. For instance, Koubarakis et al [70] hold a view that searching, integration and using big spatial data in other applications could be improved significantly if they are published using geospatial semantic web concepts. As a proof of concept, they generated a methodology showing clear benefits in a case-study of wild-fire-monitoring service.…”

Section: Big Geospatial Data and The Geospatial Semantic Webmentioning

confidence: 99%

A Survey of Geospatial Semantic Web for Cultural Heritage

2019

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The amount of digital cultural heritage data produced by cultural heritage institutions is growing rapidly. Digital cultural heritage repositories have therefore become an efficient and effective way to disseminate and exploit digital cultural heritage data. However, many digital cultural heritage repositories worldwide share technical challenges such as data integration and interoperability among national and regional digital cultural heritage repositories. The result is dispersed and poorly-linked cultured heritage data, backed by non-standardized search interfaces, which thwart users’ attempts to contextualize information from distributed repositories. A recently introduced geospatial semantic web is being adopted by a great many new and existing digital cultural heritage repositories to overcome these challenges. However, no one has yet conducted a conceptual survey of the geospatial semantic web concepts for a cultural heritage audience. A conceptual survey of these concepts pertinent to the cultural heritage field is, therefore, needed. Such a survey equips cultural heritage professionals and practitioners with an overview of all the necessary tools, and free and open source semantic web and geospatial semantic web platforms that can be used to implement geospatial semantic web-based cultural heritage repositories. Hence, this article surveys the state-of-the-art geospatial semantic web concepts, which are pertinent to the cultural heritage field. It then proposes a framework to turn geospatial cultural heritage data into machine-readable and processable resource description framework (RDF) data to use in the geospatial semantic web, with a case study to demonstrate its applicability. Furthermore, it outlines key free and open source semantic web and geospatial semantic platforms for cultural heritage institutions. In addition, it examines leading cultural heritage projects employing the geospatial semantic web. Finally, the article discusses attributes of the geospatial semantic web that require more attention, that can result in generating new ideas and research questions for both the geospatial semantic web and cultural heritage fields.

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Managing Big, Linked, and Open Earth-Observation Data: Using the TELEIOS\/LEO software stack

Cited by 37 publications

References 13 publications

GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

A Survey of Geospatial Semantic Web for Cultural Heritage

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