Abstract
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”.
Highlights
Coastal zones and oceans are the subjects of a vast and increasing number of studies whose purpose is to prevent or manage disasters, the sustainable management of coastal areas and oceans, and marine safety
We focus on an integrated process that: (a) extracts semantic knowledge from Earth Observation (EO) images, (b) models this knowledge using a geo-ontology for marine phenomena, and (c) applies question answering techniques on a semantically enabled knowledge base that allow users to express their needs and issue queries in natural language
We have presented SeMaRe, a semantic marine retrieval framework that aims to allow users to retrieve information regarding marine phenomena annotated on EO
Summary
Coastal zones and oceans are the subjects of a vast and increasing number of studies whose purpose is to prevent or manage disasters, the sustainable management of coastal areas and oceans, and marine safety. Several studies develop Remote Sensing (RS) methods and techniques—such as processing of Earth Observation (EO) satellite images (indices, classifications, object-based image analysis, etc.), mathematical simulation models, and deep learning—for better monitoring, understanding, and forecasting natural or human-induced marine phenomena. These techniques are integrated with Geographic Information Systems (GIS) that allows the implementation of static, live or forecasting spatio-temporal analysis and the production of useful products like sea wind/waves, sea temperature, sea color, spatial distribution of the sea species, seasonal cycle of microorganisms (based on temperature, sunlight, currents, and presence of polluting species), oil spill detection etc.
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