Using ontological inference and hierarchical matchmaking to overcome semantic heterogeneity in remote sensing-based biodiversity

Ontology-based applications hold promise in improving spatial data interoperability. In this work we use remote sensing-based biodiversity information and apply semantic formalisation and ontological inference to show improvements in data interoperability/comparability. The proposed methodology includes an observation-based, “bottom-up” engineering approach for remote sensing applications and gives a practical example of semantic mediation of geospatial products. We apply the methodology to three different nomenclatures used for remote sensing-based classification of two heathland nature conservation areas in Belgium and Germany. We analysed sensor nomenclatures with respect to their semantic formalisation and their bio-geographical differences. The results indicate that a hierarchical and transparent nomenclature is far more important for transferability than the sensor or study area. The inclusion of additional information, not necessarily belonging to a vegetation class desc...

• Type: Journal
• Year: 2015
• Where: AEOG
• Authors: Simon Nieland, Birgit Kleinschmit, Michael Förster
• Keywords: AEOG 2015, Artificial Intelligence