Ontologies to interpret remote sensing images: why do we need them?

Abstract

The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts' expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

“…By this way, a same concept can be mapped to different numerical definitions associated to different types of data. As mentioned by Arvor et al (2019) [67], the use of ontologies does not improve classification results but provides a more reliable representation of expert knowledge. A geographical object is defined by its characteristics in a domain ontology.…”

“…While most methods are data-driven, the abundance of data in different formats and the need for generic approaches require more knowledge-driven approaches that would allow for the integration of expert knowledge in the process. For that purpose, domain ontologies are relevant since ontologies can store knowledge and help sharing this knowledge [67]. Ontologies can include both human concepts, based on cognition, and their translation into numerical concepts required for processing.…”

Expansion of Rural Settlements on High-Quality Arable Land in Tongzhou District in Beijing, China

“…For the land use map of 2015, the classification accuracy of water bodies was as high as 100%, followed by arable land (95.7%), forest and grass land (81.8%), rural settlement (81.8%), urban land (80%), and other construction land (71.4%). As a whole, the classification accuracy for land use maps for 1972, 1991, and 2015 was high according to the accuracy requirements for image classification [54,55].…”

“…In the first sub-stage, the area ratio of the produced map was compared with that of reference data to assess the accuracy [54]. The limitation of this method was the lack of the validation of the location [55]. In the second sub-stage, the accuracy assessment focused on the comparison of site specifics of the land use type and accuracy metrics.…”

Centrais Hidrocinéticas Como Meio Para a Reestruturação Democrática Do Setor Elétrico

“…After researching most popular scientific sources, very few ontology research works related to aerospace engineering and even less for satellite development were found. Among those works we can mention a few: [Arvor et al 2019 Those can be easily understood by applying an analogy to computer programming languages variables and its types. For example, A numeric variable with value 10 is of the type number.…”