An automatic hierarchical image classification scheme
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Learning primitive and scene semantics of images for classification and retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Semantic Granularity in Ontology-Driven Geographic Information Systems
Annals of Mathematics and Artificial Intelligence
Semantic Analysis and Recognition of Raster-Scanned Color Cartographic Images
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Fiat and Bona Fide Boundaries: Towards on Ontology of Spatially Extended Objects
COSIT '97 Proceedings of the International Conference on Spatial Information Theory: A Theoretical Basis for GIS
Methodologies, tools and languages for building ontologies: where is their meeting point?
Data & Knowledge Engineering
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Expert Systems with Applications: An International Journal
Encyclopedia of GIS
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In this paper, we present FERD, a methodology aimed to automatically identify, extract and describe relevant spatial objects contained in raster spatial datasets. Our objective is to provide a set of computational tools capable of finding landforms contained in the datasets that match human-friendly descriptions such as ''In this model there is a mountain having a maximum altitude of 302m, located between coordinates (19.09383^oN, 99.85541^oW) and (19.09393^oN, 99.85554^oW)''. The proposed methodology is composed of three main stages: in the first stage (conceptualization), the knowledge domain is represented by means of ontologies. In the second stage (synthesis) a novel semantic decomposition algorithm is used to identify and extract relevant spatial objects from the spatial dataset. In the last stage (description), the geographic objects extracted in the second stage are mapped to concepts (objects of the knowledge domain) generated in the first stage. The final result is a set of metadata that describes the geomorphologic objects contained in the raster dataset.