An Ontological Model for the Automated Data Replenishment Method Implementation for the Synthesis of Applied Photogrammetry Systems

The article deals with the development of a modified ontological model for the formation and application of a knowledge base for the selection of methods for solving problems of applied photogrammetry in various fields, such as mapping, archaeology and aerial photography. This is relevant, in particular, for automating the design of problem-oriented systems of applied photogrammetry, as well as for fast tracking of new methods, which is difficult to perform manually. The proposed solution is based on the use of a fuzzy ontology containing information about different photogrammetry methods, imaging conditions, equipment parameters and desired results. To integrate new methods into the knowledge base it is proposed to use a combination of modern methods of natural language processing and machine learning, and to store the information about the methods in the form of text descriptions. This allows to automatically determine the optimal methods for a particular photogrammetry task, taking into account the conditions of surveying and the desired results. The paper describes in detail the structure of the ontology with modifications that take into account the current trends in the subject area, in particular the use of mobile devices, as well as the algorithm for integrating new methods and the potential advantages of the proposed solution. The paper also describes the potential of the developed system to improve the efficiency, accuracy and automation of method selection in photogrammetry, which can significantly improve the results of practical applications of photogrammetry in various fields.