Abstract
The paper proposes a novel perspective on Web discovery services of remote sensing images and derived products currently available through the Web portals of providers managing big geo-spatial data repositories. Actual discovery services do not provide facilities for ranking images based on queries specifying spatial-content conditions, i.e. asking for images having desired pixels values in a Region Of Interest (ROI). Our objective is to enable such a facility by designing both a query language with linguistic terms to ask for the desired qualitative characteristics of the image content in a ROI, and a retrieval mechanism to evaluate the degrees of satisfaction of the images with respect to the query spatial-content conditions. The retrieval mechanism is implemented as a meta-discovery service, i.e., as a front-end on the discovery service of the image provider, that does not need to access the images, but just their previews, empowering the retrieval with ranking capabilities. It requires a spatial-content inverted index, previously built off-line by processing all image previews so as to achieve scalability and retrieval efficiency.
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Acknowledgements
The present work was carried out within the SIMULATOR-ADS project funded by Regione Lombardia grant POR FESR 2014-2020 and STRESS project, funded by Fondazione Cariplo 2016.
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Gloria, B., Andrea, C., Simone, S. (2017). Content-Based Meta-Discovery Service of Remote Sensing Images. In: Christiansen, H., Jaudoin, H., Chountas, P., Andreasen, T., Legind Larsen, H. (eds) Flexible Query Answering Systems. FQAS 2017. Lecture Notes in Computer Science(), vol 10333. Springer, Cham. https://doi.org/10.1007/978-3-319-59692-1_13
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