Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images
Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated...
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doaj-3a2c9af1650548579617dcde5cf242ad2021-08-26T13:54:12ZengMDPI AGInformation2078-24892021-08-011232132110.3390/info12080321Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite ImagesVasilis Kopsachilis0Lucia Siciliani1Marco Polignano2Pol Kolokoussis3Michail Vaitis4Marco de Gemmis5Konstantinos Topouzelis6Department of Geography, University of the Aegean, GR-81100 Mytilene, GreeceDepartment of Computer Science, University of Bari Aldo Moro, I-70126 Bari, ItalyDepartment of Computer Science, University of Bari Aldo Moro, I-70126 Bari, ItalyLaboratory of Remote Sensing, Department of Topography, School of Rural, Surveying and Geomatics Engineering, National Technical University of Athens, GR-15780 Zografou, GreeceDepartment of Geography, University of the Aegean, GR-81100 Mytilene, GreeceDepartment of Computer Science, University of Bari Aldo Moro, I-70126 Bari, ItalyDepartment of Marine Sciences, University of the Aegean, GR-81100 Mytilene, GreeceScientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”.https://www.mdpi.com/2078-2489/12/8/321marine phenomenasatellite imagesremote sensing processingsemantic annotationontologiesquestion answering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vasilis Kopsachilis Lucia Siciliani Marco Polignano Pol Kolokoussis Michail Vaitis Marco de Gemmis Konstantinos Topouzelis |
spellingShingle |
Vasilis Kopsachilis Lucia Siciliani Marco Polignano Pol Kolokoussis Michail Vaitis Marco de Gemmis Konstantinos Topouzelis Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images Information marine phenomena satellite images remote sensing processing semantic annotation ontologies question answering |
author_facet |
Vasilis Kopsachilis Lucia Siciliani Marco Polignano Pol Kolokoussis Michail Vaitis Marco de Gemmis Konstantinos Topouzelis |
author_sort |
Vasilis Kopsachilis |
title |
Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images |
title_short |
Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images |
title_full |
Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images |
title_fullStr |
Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images |
title_full_unstemmed |
Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images |
title_sort |
semantically-aware retrieval of oceanographic phenomena annotated on satellite images |
publisher |
MDPI AG |
series |
Information |
issn |
2078-2489 |
publishDate |
2021-08-01 |
description |
Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”. |
topic |
marine phenomena satellite images remote sensing processing semantic annotation ontologies question answering |
url |
https://www.mdpi.com/2078-2489/12/8/321 |
work_keys_str_mv |
AT vasiliskopsachilis semanticallyawareretrievalofoceanographicphenomenaannotatedonsatelliteimages AT luciasiciliani semanticallyawareretrievalofoceanographicphenomenaannotatedonsatelliteimages AT marcopolignano semanticallyawareretrievalofoceanographicphenomenaannotatedonsatelliteimages AT polkolokoussis semanticallyawareretrievalofoceanographicphenomenaannotatedonsatelliteimages AT michailvaitis semanticallyawareretrievalofoceanographicphenomenaannotatedonsatelliteimages AT marcodegemmis semanticallyawareretrievalofoceanographicphenomenaannotatedonsatelliteimages AT konstantinostopouzelis semanticallyawareretrievalofoceanographicphenomenaannotatedonsatelliteimages |
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1721192462652801024 |