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Up to date and accurate urban land cover information is needed in a variety of applications, e.g. urban planning and management. However, depending on traditional surveying tools, especially in large metropolitan cities, to produce such date is a time consuming and expensive task. This has initiated the need to classify remotely sensed data to extract urban(More)
This paper presents a proposed model regarding Heterogeneous Data Reduction. The model reduces data over a heterogeneous environment through feature selection/extraction. The feature is selected/extracted directly from its data source and prepared without an initial integration for all data sources. After that the selected/extracted prepared feature is(More)
Many image understanding systems rely heavily on a priori knowledge of their domain of application, drawing parallels with and exploiting techniques developed in the wider field of knowledge-based systems (KBSs). Attempts, typified by the KADS/CommonKADS projects, have recently been made to develop a structured, knowledge engineering approach to KBS(More)
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