Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface
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 program...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Russian |
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Ministry of Education of the Republic of Belarus, Establishment The Main Information and Analytical Center
2024-03-01
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| Series: | Цифровая трансформация |
| Subjects: | |
| Online Access: | https://dt.bsuir.by/jour/article/view/821 |
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| _version_ | 1832557194594222080 |
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| author | T. F. Starovoitova I. A. Starovoitov |
| author_facet | T. F. Starovoitova I. A. Starovoitov |
| author_sort | T. F. Starovoitova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bf22c114b5f2416e87ae617e8f404133 |
| institution | Kabale University |
| issn | 2522-9613 2524-2822 |
| language | Russian |
| publishDate | 2024-03-01 |
| publisher | Ministry of Education of the Republic of Belarus, Establishment The Main Information and Analytical Center |
| record_format | Article |
| series | Цифровая трансформация |
| spelling | doaj-art-bf22c114b5f2416e87ae617e8f4041332025-02-03T05:39:06ZrusMinistry of Education of the Republic of Belarus, Establishment The Main Information and Analytical CenterЦифровая трансформация2522-96132524-28222024-03-01301637010.35596/1729-7648-2024-30-1-63-70329Machine Learning Model for Processing Aerospace Images of the Earthʼs SurfaceT. F. Starovoitova0I. A. Starovoitov1Academy of Public Administration under the President of the Republic of BelarusRepublican Design Institute for Land Management «Belgiprozem»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.https://dt.bsuir.by/jour/article/view/821aerospace imagesmachine learningneural networksdata processingobject recognitionpythontensorflow |
| spellingShingle | T. F. Starovoitova I. A. Starovoitov Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface Цифровая трансформация aerospace images machine learning neural networks data processing object recognition python tensorflow |
| title | Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface |
| title_full | Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface |
| title_fullStr | Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface |
| title_full_unstemmed | Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface |
| title_short | Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface |
| title_sort | machine learning model for processing aerospace images of the earth s surface |
| topic | aerospace images machine learning neural networks data processing object recognition python tensorflow |
| url | https://dt.bsuir.by/jour/article/view/821 |
| work_keys_str_mv | AT tfstarovoitova machinelearningmodelforprocessingaerospaceimagesoftheearthʼssurface AT iastarovoitov machinelearningmodelforprocessingaerospaceimagesoftheearthʼssurface |