COMBINED OBJECT-ORIENTED AND NEURAL NETWORK APPROACHES FOR SPATIAL DATA MODELS

RECENT DEVELOPMENTS EMPLOYING OBJECT-ORIENTED DATA MODELS HAVE DEMONSTRATED THE FEASIBILITY OF REPRESENTING COMPLEX AND ABSTRACT CARTOGRAPHIC FEATURES AS COMPOSITES OF SIMPLER FEATURES. SEPARATE DEVELOPMENTS IN NEURAL-NETWORK TECHNOLOGY HAVE SHOWN PROMISE IN RECOGNIZING OR EXTRACTING SPATIAL FEATURES WITHIN SAMPLE DATA SETS. COMBINING OBJECT-ORIENTED AND NEURAL-NETWORKS APPROACHES MAY LEAD TO NEW METHODS OF CAPTURING, STORING, AND REPRESENTING HIGHER ORDER CARTOGRAPHIC KNOWLEDGE ASSOCIATED WITH SPATIAL FEATURES. GENERIC OBJECT-ORIENTED DATA MODES MAY BE USED TO STRUCTURE AND MANAGE SPATIAL DATA BASES. THESE MODELS WILL ALLOW SPATIAL DATA PRIMITIVES TO BE GROUPED INTO OBJECT CLASSES CONTAINING DATA, ATTRIBUTES, AND HIERARCHICAL OBJECT RELATIONSHIPS. ADDITIONALLY, HIGHER LEVEL (APPLICATION LEVEL) FEATURES CAN BE COMPOSED FROM ELEMENTS OF THE GENERIC OBJECT MODE. GENERIC OBJECT MODELS NEED TO BE DEVELOPED IN AN APPLICATION AND DATA INDEPENDENT MANNER. NEURAL NETWORKS PROVIDE A PARALLEL MAPPING OF EVENTS OR DATA, VIA HIDDEN PROCESSING ELEMENTS, TO A PROBABLE OUTPUT NODE OR RESULT. NEURAL NETWORKS HAVE BEEN USED FOR CHARACTER RECOGNITION, WHERE A SAMPLE CHARACTER (USUALLY IN THE FORM OF AN IMAGE) IS PRESENTED AS INPUT; THE DESIRED OUTCOME IS THE CORRECT CLASSIFICATION OF THE CHARACTER. QUBA, INC. PROPOSESS TO PERFORM RESEARCH AND EXPLORATORY DEVELOPMENTS FOR COMBINING OBJECT-ORIENTED AND NEURAL-NETWORK CAPABILITIES FOR COMBINING OBJECT-ORIENTED AND NEURAL-NETWORK CAPABILITIES FOR CAPTURING, STORING, AND REPRESENTING CARTOGRAPHIC KNOWLEDGE. THIS RESEARCH WILL EXAMINE NAUTICAL CARTOGRAPHIC DATA AND APPLICATIONS, BUT MAY EASILY BE EXTENDED TO ADDITIONAL SPATIAL DATA AND GEOGRAPHIC MAPPING APPLICATIONS.