LARGE LANGUAGE MODELS APPLICATION IN ORGANIZATION OF REPLENISHMENT OF THE KNOWLEDGE BASE ON METHODS OF INFORMATION PROCESSING IN SYSTEMS OF APPLIED PHOTOGRAMMETRY

Abstract

The article deals with the issues related to the automation of the procedure of synthesis of applied photogrammetry systems. Such systems serve to measure and account for objects from images and are now widely utilized in various fields of activity, such as mapping, archaeology and aerial photography. Increasing availability and mobility of imaging devices has also contributed to the widespread application. All this has led to active research aimed at developing methods and algorithms for applied photogrammetry systems. Manual tracking of new methods and algorithms of photogrammetric information processing for a wide range of application areas is quite difficult, which makes the automation of this procedure urgent. The solution proposed in the article is based on the use of a knowledge base of information processing methods in applied photogrammetry systems, the main elements of which are a fuzzy ontology of the subject area and a database, which is logical, since the information about the subject area can be structured quite easily. As a basis for the ontology, an existing solution was taken, which was supplemented based on the results of analyzing the current state of the subject area. The resulting ontology was further used to search and classify information processing methods in applied photogrammetry systems and to populate the knowledge base. Due to the intensification of the development of new methods of information processing in the systems of applied photogrammetry, there is a need to modify the ontology and to replenish the database, i.e. to replenish the knowledge base. The Internet is an important source of information for this purpose. To automate the search for data on information processing methods and ontology modification, it is reasonable to use large language models. To automate data mining of information processing methods and to populate the knowledge base, it is useful to use large language models that simplify several natural language processing tasks, which include clustering and formation of new entities for classification. The corresponding method is described in the paper. For the method the results of testing its performance are given. As part of problem solving, a comparative analysis of large language models has been carried out, resulting in the RoBERTa model.