Author Description

Computer-assisted classification of remotely sensed data into qualitative classes is useful for extracting information that can be exploited for cartographic purposes, such as in the generation of thematic maps of land cover types. For a proper cartographic application, the fitness for use of a set of remotely sensed data needs be assessed. For assessing the fitness for use of a set of remotely sensed data, accuracy is not the only consideration. A high quality indicates a relatively high information value for the considered application - a good fitness for use. Uncertainty is a key-issue in quality assessment and, therefore, in the assessment of fitness for use of a data set. Besides the assessment of uncertanty, efforts can be aimed at the reduction of the amount of uncertainty present in a remotely sensed data set. The probabilistic results from the classification procedure and other quality information are subjected to cartographic visualisation rules in order to develop a framework for the communication of this spatial metadata. Static as well as more dynamic approaches offer grips for the gis user who needs to consider simple but persuasive maps to assess the fitness for use of a classification.