Very-High-Resolution SAR Images and Linked Open Data Analytics Based on Ontologies

In this paper, we deal with the integration of multiple sources of information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors of the image content, as well as other publicly available geospatial data sources expressed as linked open data for posing complex queries in order to support geospatial data analytics. Our approach lays the foundations for the development of richer tools and applications that focus on EO image analytics using ontologies and linked open data. We introduce a system architecture where a common satellite image product is transformed from its initial format into to actionable intelligence information, which includes image descriptors, metadata, image tiles, and semantic labels resulting in an EO-data model. We also create a SAR image ontology based on our EO-data model and a two-level taxonomy classification scheme of the image content. We demonstrate our approach by linking high-resolution TerraSAR-X images with information from CORINE Land Cover (CLC), Urban Atlas (UA), GeoNames, and OpenStreetMap (OSM), which are represented in the standard triple model of the resource description frameworks (RDFs)

Artificial Intelligence Data Science Methodology for Earth Observation

This chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligence (AI) methods. From 2020, the platform will be applied at a regional and national level to various use cases such as urban expansion, forest health, and natural disasters. Its workflows allow the selection of satellite images from data archives, the extraction of useful information from the metadata, the generation of descriptors for each individual image, the ingestion of image and descriptor data into a common database, the assignment of semantic content labels to image patches, and the possibility to search and to retrieve similar content-related image patches. The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images

Earth Observation Semantics and Data Analytics for Coastal Environmental Areas

Current satellite images provide us with detailed information about the state of our planet, as well as about our technical infrastructure and human activities. A range of already existing commercial and scientific applications try to analyze the physical content and meaning of satellite images by exploiting the data of individual, multiple or temporal sequences of images. However, what we still need today are advanced tools to automatically analyze satellite images in order to extract and understand their full content and meaning. To remedy this exploration problem, we outline a highly automated and application-adapted data-mining and content interpretation system consisting of five main components, namely Data Sources (selection and storage of relevant images), Data Model Generation (patch cutting and generation of feature vectors), Database Management System (systematic data storage), Knowledge Discovery in Databases (clustering and content labeling), and Statistical Analytics (generation of classification maps). As test sites, we selected UNESCO-protected areas in Europe that include coastal areas for monitoring and an area known in the Mediterranean Sea that contains fish cages. The analyzed areas are: the Curonian Lagoon in Lithuania and Russia, the Danube Delta in Romania, the Hardangervidda in Norway, and the Wadden Sea in the Netherlands. For these areas, we are providing the results of our image content classification system consisting of image classification maps and additional statistical analytics based on three different use cases. The first use case is the detection of wind turbines vs. boats in the Wadden Sea. The second use case is the identification of fish cages/aquaculture along the Mediterranean coast. Finally, the third use case describes the differences between beaches, dams, dunes, and tidal flats in the Danube Delta, the Wadden Sea, etc. The average classification accuracy that we obtained is ranging from 80% to 95% depending on the type of available images