The paper presents the least admissible dimensions of black lines of spatial object images, according to Saliszczew, adjusted to the needs of database generalization. It is pointed out, that the adjusted dimensions are in agreement with the cartographic norm included in the National Map Accuracy Standards , and their application to the generalization 1 will allow, for any map scale, the determination of the: • value of the scale-dependent parameter of the generalization process, without user action; • measure of recognizability of the shortest black line section on the map, what helps to obtain unique results of line generalization; • measure of recognizability of black lines in the image – using a standard (elementary triangle) – helpful in obtaining unique result of line simplification, and an assessment of the process; • recognizability distance between lines of close buildings, securing unique aggregation of them; • verification of spatial object image lines visualization. The new solutions were tested with the Douglas-Peucker (1973) generalization algorithm, modified by the author, which treats the minimal dimensions as geometric attributes, while object classes and their data hierarchy as descriptive attributes. This approach secures uniqueness of results on any level of generalization process, in which data of spatial objects in the DLM model are transformed to conform with the requirements for the DCM model data.