Current study is a part of the research which is aimed at identifying approaches to the recognition of spatial data objects presented in the vector data models. In the previous stage the classification of spatial objects was based on the set of their morphometric features—shape characteristics. A conclusion was drawn that such attributes were insufficient for automated recognition and there was a need in additional study of the spatial relationships of objects. That means the transition from the object recognition level to the feature classes recognition level, from shape analysis to the exploraton of objects combinations—patterns. It is important to analyse spatial relationships between objects of the same classes as well as different ones. That study covers the problem of classifying various feature classes.

Application options of the study results are described in the paper on a par with the methodology of solving the task and materials involved in the study. Moreover, the results of objecs recognition based on their morphometric features and graph characteristics are compared.

The paper presents the ways of automated recognition of natural and antropogenic vector objects with the linear localization. The study applies Graph theory and Machine learning to classify them. The Python script for calculating graph parameters of linear objects was created. Furthermore, the model in Rapid Miner Studio application program was prepared for spatial objects’ nets recognition.