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

Abstract

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).

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

“…Our approach here is close to these studies in the sense that we guide the integration process on the top of an ontology reusing existing standards. Another close proposal is the one from [3], which carries out an ETL process to integrate EO image and external data sources, such as CLC, Urban Atlas, and Geonames. The process relies on their SAR ontology.…”

“…Land cover data can then be useful for the study of crop evolution, the progress of urban areas, or the impact of natural hazards. Moreover, the semantic representation of land cover data has been exploited for image annotation improving semantic search [3,4].…”

Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

“…Others reported on the analysis and implementation of frameworks for the assessment of OSM data [102][103][104][105]. The research trend "land-use patterns" (T50.6) was reported in thirteen articles that were focused on the use of OSM in remote sensing applications, particularly land use mapping [106][107][108][109][110][111][112][113][114]. Another trend that emerged was "indoor navigation" (T50.2), which focused on mobile enabled indoor navigation in transport services [65,66,115] and their augmentation with floor plans [68].…”

“…This effect may be attributed to the use of dominating keywords in these articles. For instance, the topic "land-use patterns" (50.6) revealed thirteen articles [106][107][108][109][110][111][112][113][114][132][133][134][135]. From these, eleven articles emerged from "quality assessment and analysis" (T5.1), whereas two of the papers were focused on "assessment of contributors' behavior" (T5.2) [110] and "indoor navigation models" (T5.5) [107], respectively.…”

Managing Big, Linked, and Open Earth-Observation Data: Using the TELEIOS\/LEO software stack

“…With the techniques of KDD framework, we can characterize satellite image regions with concepts from appropriate ontologies (e.g., land cover ontologies with concepts such as water body, lake, and forest or environmental monitoring ontologies with concepts such as forest fires and flood) [12]- [14]. These concepts are encoded in OWL ontologies and are used to annotate EO products.…”