ABSTRACT This paper presents a novel approach to assessing the geometry of objects using Hu's invariants in the context of cartographic generalization. The primary focus is to improve the generalization process and produce more readable and informative maps. The study demonstrates the applicability and effectiveness of the modified invariant moment M1* in evaluating regular shape similarity. Experiments, based on 24 shapes, exhibit greater stability in the results and reveal the high suitability of this moment in the investigation and classification of buildings, among other generalization processes. The efficiency of the proposed method is compared to previous generalization techniques, showing a significant improvement in the generalization process. In conclusion, this research contributes to the development of cartographic generalization methods by introducing the use of Hu's invariants for evaluating object geometry. This approach can improve the automation of map generalization processes and more effective communication of geographic information.