The Role of Artificial Intelligence in the Integration of Remotely Sensed Data with Geographic Information Systems
Year: 1987
Type: article
Abstract: Although databases for geographic information systems (GIS) have been developed to manage digital map data, the integration of remotely sensed imagery and other collateral non-map information is rarely performed. For the most part, the use of sophisticated intelligent spatial databases, in which the user can query interactively about map, terrain, or associated imagery, is unknown in the GIS and cartographic community. In standard GIS systems, the ability to formulate complex queries requiring dynamic computation of factual and geometric properties is severely limited, often reflecting its origin as collections of thematic map overlays. Spatial database research requires the integration of ideas and techniques from many disciplines such as computer graphics, computational geometry, database methodology, image analysis, photogrammetry, and artificial intelligence. In this paper we discuss some ideas on how the scope of geographic information systems can be expanded by utilizing techniques from the Al community that may remedy deficiencies in user interfaces, spatial data representation, and its utilization. We draw on ongoing research at Carnegie Mellon University for examples of these techniques in the areas of image/map database and knowledge-based image interpretation.
Author: David Mckeown
Institution: Carnegie Mellon University
Language: English
Subfield: Geography, Planning and Development
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2130059162