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Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation
Örebro University, School of Science and Technology. (Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-4001-2087
Örebro University, School of Science and Technology. (Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-0579-7181
Department of Computer Science, Aalto University, Espoo, Finland.
Örebro University, School of Science and Technology. (Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-3122-693X
2019 (English)In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 10, no 5, p. 863-880Article in journal (Refereed) Published
Abstract [en]

Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections. The semantic referee relies on ontological reasoning about spatial knowledge in order to characterize errors in terms of their spatial relations with the environment. Using semantics, the reasoner interacts with the learning algorithm as a supervisor. In this paper, the proposed method of the interaction between a neural network classifier and a semantic referee shows how to improve the performance of semantic segmentation for satellite imagery data.

Place, publisher, year, edition, pages
IOS Press, 2019. Vol. 10, no 5, p. 863-880
Keywords [en]
Deep Neural Network, Semantic Referee, Ontological and Spatial Reasoning, Semantic Segmentation, OntoCity, Geo Data
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-77266DOI: 10.3233/SW-190362ISI: 000488082100003Scopus ID: 2-s2.0-85072400163OAI: oai:DiVA.org:oru-77266DiVA, id: diva2:1360569
Projects
Semantic Robot
Funder
Swedish Research Council
Note

Funding Agency:

Swedish Knowledge Foundation under the research profile on Semantic Robots  20140033

Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2024-02-06Bibliographically approved

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Alirezaie, MarjanLängkvist, MartinLoutfi, Amy

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