The use and dissemination of remotely-sensed data is an important resource that can be used for environmental, commercial and educational purposes. Because of this, the use and availability of remotely-sensed data has increased dramatically in recent years. This usefulness, however, is often overshadowed by the difficulty encountered with trying to deal with this type of data. The amount of data available is immense. Storing, searching and retrieving the data of interest is often difficult, time consuming and inefficient. This is particularly true when these types of data need to be rapidly and continually accessed via the Internet, or combined with other types of remotely-sensed data, such as combining Aerial Photography with US Census vector data. This thesis addresses some of these difficulties, a two-fold approach has been taken. First, a database schema which can store various types of remotely-sensed data in one database has been designed for use in a Semantic Object-Oriented Database System (Sem-ODB). This database schema includes in its design a linear addressing scheme for remotely-sensed objects which maps an object’s 2-dimentional (latitude/longitude) location information to a 1-dimensional integrated integer value. The advantages of using this Semantic schema with remotely-sensed data is discussed and the use of this addressing scheme to rapidly search for and retrieve point-based vector data is investigated. In conjunction with this, an algorithm for transforming a remotely-sensed range search into a number of linear segments of objects in the 1-dimensional array is investigated. The main issues and the combination of solutions involved are discussed
