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 reasons including the needs for recognition categories not defined as standard categories or for specialized data displays.

The argument can be made that, besides the general desirability of system flexibility, it is important to give advanced users a flexible, complex, and adaptive tool. Very likely some of the most innovative and important IESIS applications will arise through the efforts of such individuals.

At present, Landsat data customers represent a relatively small but sophisticated population comprised mainly of engineers and scientists. IESIS is intended to reach a much broader spectrum of potential users, the majority of whom are "naive" with regard to computer technology. It is imperative that a reasonable model of this target population be generated as the system is implemented. Such knowledge is necessary for detailed design of user/system interfaces, ensuring insertion of user-related elements into the world model, and for determining signature analysis and pattern recognition techniques required to answer probable user questions.

2.2.8 Data Archiving and Compaction

The traditional NASA information gathering philosophy has been to collect as much raw data as possible from each mission and then allow university, industrial, and government researchers to complete the analysis. In the early days of space exploration this strategy was reasonable, based on spacecraft investment, insofar as it maximized return. But today, advancing satellite technology has greatly expanded the number of sensors flown and available data rates. The resulting torrential flow of information has overwhelmed the capacity of the system - only 0.05% of data collected from space has ever been analyzed. The great unused bulk of observations must be stored even though much of it is of marginal quality (e.g., obscured by clouds) and probably never will be analyzed.

NASA should consider revising its philosophy of data collection to: (1) Make use of knowledge gained from previous sensing missions to reduce redundancy, (2) adopt a goal-oriented approach to Earth-sensing and other observational missions, (3) begin to identify and dispose of poor- quality data, (4) condense information as it ages and becomes less useful, and (5) present data with full indexing and cross-referencing to maximize their utility to the consumer.

Knowledge and experience combined with artificial intelligence techniques can eliminate redundancy. For example, it is extremely inefficient for an Earth-sensing satellite to "rediscover" a lake, road, or city on every orbital pass. The truly important aspects of the object are its fundamental attributes area, temperature, color, texture, etc. - many of which are either constant or predictable. The use of a world model to eliminate continual rediscovery of such features could greatly reduce the extraordinary redundancy of most visual imagery.

All object attributes studied must reflect worthwhile goals. One goal should be the assembly of a historical record of Earth features. Others may include more specific user-defined objectives. This new emphasis on goal-directed observations does not preclude data utilization by the scientific community in the investigation and verification of new theories; quite the contrary, it should actually enhance this activity by enabling researchers quickly and easily to direct IESIS to collect and process data under closely controlled conditions.

Many time-oriented observations lose some of their value with age. After an extended period of time, long-term trends are much more useful than individual data values. In the proposed IESIS system only the long-term trends are retained original data are eventually discarded. Thus, the system processes all data immediately for specific goals and, at a later time, integrates trend information into a more compact world model representation such as a long-term temperature gradient.

As data are collected in orbit, Earth features and their processed image characteristics must be fully indexed to sort features and characteristics and to analyze them by group. The data then may be manipulated from within a fully cross-referenced base. For instance, area type can be called out and summed to obtain the total rye acreage in a given state. This cross-referencing feature is critical to the effectiveness of the Earth-sensing information system. The ability to automatically cross-reference and access data by content and feature allows rapid aggregation and correlation, and may promote new research as rapid access to useful scientific data becomes routine. The proposed database is organized using geographic location as the primary key (similar to the World Reference System used for Landsat data) with individual features also keyed. Feature keys greatly simplify the generation of inverted files listing, say, all lakes, deserts, wetlands, forests, or housetop areas obvious widespread applications. The detailed mechanisms of records layouts, file structures, and database languages are beyond the scope of the present study and are recommended for future investigation.

The expense of storing data is a very significant part of computer system cost. This consists of direct charges for storage media as well as the costs of transferring data to and from local and remote storage devices. Data compaction and compression produce cost savings by reducing storage and transmission requirements. In addition, these data reduction methods enable more efficient information retrieval and more economical transmission of large quantities of data over computer networks.