Building Virtual Earth Observatories Using Ontologies and Linked Geospatial Data

Koubarakis, Manolis; Karpathiotakis, Manos; Kyzirakos, Kostis; Nikolaou, Charalampos; Vassos, Stavros; Garbis, George; Sioutis, Michaël; Bereta, Konstantina; Manegold, Stefan; Kersten, Martin; Ivanova, Milena; Pirk, Holger; Zhang, Ying; Kontoes, Charalampos; Papoutsis, Ioannis; Herekakis, Themistoklis; Mihail, Dimitris; Datcu, Mihai; Schwarz, Gottfried; Dumitru, Octavian; Molina, Daniela Espinoza; Molch, Katrin; Giammatteo, Ugo Di; Sagona, Manuela; Perelli, Sergio; Klien, Eva; Reitz, Thorsten; Gregor, Robert J.

doi:10.1007/978-3-642-33203-6_21

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…The vision of TELEIOS has been discussed in [10,12] and a demo of the fire monitoring service has been presented in [11,13].…”

Section: Related Workmentioning

confidence: 99%

“…The ontology is encoded in OWL and it is publicly available 11 . The main classes of the current version of the NOA ontology, which is depicted graphically in Figure 5, are RawData, Shapefile, and Hotspot, which represent files with raw data (e.g., sensor measurements), ESRI shapefiles, which are the outputs of the hotspot detection processing chain and hotspots, which are extracted from shapefiles, respectively.…”

Section: Ontology For Noa Standard Productsmentioning

confidence: 99%

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Real-time wildfire monitoring using scientific database and linked data technologies

Koubarakis

Kontoes

Manegold

2013

Proceedings of the 16th International Conference on Extending Database Technology

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We present a real-time wildfire monitoring service that exploits satellite images and linked geospatial data to detect hotspots and monitor the evolution of fire fronts. The service makes heavy use of scientific database technologies (array databases, SciQL, data vaults) and linked data technologies (ontologies, linked geospatial data, stSPARQL) and is implemented on top of MonetDB and Strabon. The service is now operational at the National Observatory of Athens and has been used during the previous summer by emergency managers monitoring wildfires in Greece.

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“…The vision of TELEIOS has been discussed in [10,12] and a demo of the fire monitoring service has been presented in [11,13].…”

Section: Related Workmentioning

confidence: 99%

Section: Ontology For Noa Standard Productsmentioning

confidence: 99%

Real-time wildfire monitoring using scientific database and linked data technologies

Koubarakis

Kontoes

Manegold

2013

Proceedings of the 16th International Conference on Extending Database Technology

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“…Also, (L, L ) assigns a label, i.e., LULC class to nodes and a spatial relation to the edges. The condition (1) implies that all the queried LULC regions exist in the RS image's SSG. Also, the condition (2) implies that the spatial relations posed by user should match with the spatial relations among the corresponding LULC regions in the SSG.…”

Section: ) Ssg and Sparql-based Subgraph Isomorphism Formentioning

confidence: 99%

“…However, the spatial relationshipsbased modeling, querying and image retrieval still remains largely unexplored. Also, it is identified in [1] that up to 95% of the currently archived EO data have never been accessed and still lies in unexploited state. Along with the unprecedented growth in the EO data archives, there is also an increasing expectation for better querying and retrieving capabilities from the EO-IIM systems, leading to new challenges.…”

mentioning

confidence: 98%

“…This results in a continuous growth of the cumulative volume of EO data archives (currently, in the order of petabytes). It is stated that the multimission data archive of TELEIOS partner German Aerospace Center (DLR) has already reached near 2 petabytes, whereas the estimated cumulative archive size of European Space Agency (ESA) will attain 20 petabytes by the year 2020 [1], [2].…”

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confidence: 99%

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Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

Kurte

Durbha

King

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recent developments in sensor technology are contributing toward the tremendous growth of remote sensing (RS) archives (currently, at the petabyte scale). However, this data largely remain unexploited due to the current limitations in the data discovery, querying, and retrieval capabilities. This issue becomes exacerbated in disaster situations, where there is a need for rapid processing and retrieval of the affected areas. Furthermore, the retrieval of images based on the spatial configurations of affected regions [land use/cover (LULC) classes] in an image is important in disaster situations such as floods and earthquakes. The majority of existing Earth observation (EO) image information mining (IIM) systems does not consider the spatial relations among image regions during image retrieval (aka spatial semantic gap). In this work, we have specifically addressed two issues, i.e., explicit modeling of topological and directional relationships between image regions, and development of a resource description framework (RDF)-based spatial semantic graphs (SSGs). This enables more intuitive querying and reasoning on the archived data. A spatial IIM (SIIM) framework isproposed, which integrates a logic-based reasoning mechanism to extract the hidden spatial relationships (both topological and directional) and enables image retrieval based on spatial relationships. The system is tested using several spatial relations-based queries on the RS image repository of flood-affected areas to check its applicability in post flood scenario. Precision, recall, and F-measure metrics were used to evaluate the performance of the SIIM system, which showed good potential for spatial relations-based image retrieval.Index Terms-Description logic (DL), direction relations, image retrieval, linked data, reasoning, region connection calculus (RCC-8), resource description framework (RDF), semantic web rules language (SWRL), SPARQL, spatial image information mining (SIIM), spatial relations, topological relations, Web ontology language (OWL).

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Building Virtual Earth Observatories Using Ontologies, Linked Geospatial Data and Knowledge Discovery Algorithms

Koubarakis¹,

Sioutis²,

Garbis³

et al. 2012

Lecture Notes in Computer Science

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Building Virtual Earth Observatories Using Ontologies and Linked Geospatial Data

Cited by 5 publications

References 4 publications

Real-time wildfire monitoring using scientific database and linked data technologies

Real-time wildfire monitoring using scientific database and linked data technologies

Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

Building Virtual Earth Observatories Using Ontologies, Linked Geospatial Data and Knowledge Discovery Algorithms

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