Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface

Abstract

The article presents the specifics of acquisition and processing aerospace images of the earth's surface in the context of their digitalization for creating accurate topographic maps and plans in digital and graphic formats. A data processing model has been developed based on the Python programming language and neural networks, the purpose of which is to improve the recognition of objects in aerospace images. The methodology for creating a machine learning model includes defining the goals and objectives of the model, selecting an appropriate learning algorithm (in this case, neural networks), collecting and preparing a data set, tuning the model, and testing on a test data set. The shortcomings of existing data processing algorithms are also discussed and an approach is presented to improve the efficiency of data processing and analysis.