ON THE QUESTION OF APPLICATION OF DEEP LEARNING FOR THE PROBLEM OF CROSS-VIEW GEOLOCALIZATION

About the magazine

News

Goals and sphere

Founder and publisher

Editorial Board

Licensing conditions

Confidentiality

Attitude towards plagiarism

Publication ethics

Archiving Policy

Subscription

For authors

Instructions for authors

The review process

Copyright

Agreement on the transfer of rights

Editorial fees

Archive

All issues

Search

Contacts

Contacts

Ermolenko Tatyana Vladimirovna
Candidate of Technical Sciences, Associate Professor, Associate Professor of the Department of Computer Technologies, Faculty of Physics and Technology, DonSU, Deputy Head of the Laboratory of Intelligent Systems and Data Analysis, IAPI.
283001, Donetsk, Universitetskaya str., 24.
Research interests: mathematical logic, fuzzy logic, natural language processing, deep learning, pattern recognition, digital signal processing.

Khakimov Renat Saitovich
Junior Researcher, Laboratory of Intelligent Systems and Data Analysis, IAPI.
283048, Donetsk, Artyoma str., 118 b.
Research interests: computer vision, machine learning, neural networks.

Download pdf

UDC 004.932.2
DOI 10.24412/2413-7383-2024-3-4-15
Language: Russian
Annotation: The article considers the problem of cross-geolocation from a mathematical point of view, highlights the main approaches, problems and key features of the application of deep learning. Problems such as 1) similarity of key objects in detail and uniform appearance are indicated; 2). image styles can vary greatly; 3) the average pool used in convolutional neural networks ignores the interaction between local objects. Among the key features of the application of deep learning are: 1) Transformer-based architectures make it possible to more clearly identify characteristic objects and ignore background information; 2) the results of a Transformer-based model trained twice will have a large spread; 3) the scale of satellite images is fixed, while the altitude of the UAV flight changes dynamically.
Keywords: cross-view geolocation, computer vision, machine learning, neural networks, UAVs.

List of literature:
1. Deuser, F., Habel, K., Werner, M., & Oswald, N. (2023). Orientation-Guided Contrastive Learning for UAV-View Geo-Localization. In UAVM 2023 - Proceedings of the 2023 Workshop on UAVs in Multimedia: Capturing the World from a New Perspective, Co-located with MM 2023 (pp. 7-11).
2. Favorskaia, M. N., & Pakhirka, A. I. (2024). Restoration of Ultra-High-Resolution Aerial Images Considering Semantic Features. Informatics and Automation, 23(4), 1047–1076.
3. Zhang, X., Jiang, M., Zheng, Z., Tan, X., Ding, E., & Yang, Y. (2020). Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4911–4920). DOI: 10.1109/CVPR42600.2020.00496
4. Lin, J., Zheng, Z., Zhong, Z., Luo, Z., Li, S., Yang, Y., & Sebe, N. (2022). Joint Representation Learning and Keypoint Detection for Cross-View Geo-Localization. IEEE Transactions on Image Processing, 31(May), 3780-3792.
5. Deuser, F., Habel, K., & Oswald, N. (2023). Sample4Geo: Hard Negative Sampling for Cross-View Geo-Localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 16801-16810).
6. Ronzhin A. L. Optimization of Technological Map of Permissible Aquaculture / A. L. Ronzhin, V. N. Le, N. Shuvalov // 2024. – Vol. 16. – No. 2. – P. 50–58.
7. Fam K. B. Fuzzy Model of Diagnostics of Technological Processes / K. B. Fam, P. M. Murashev, V. N. Bogatikov // Pattern Recognition and Image Analysis. – 2024. – Vol. 34. – No. 3. – P. 632–638.
8. Dvornikov S. V., Vasilieva D. V. Improving the Reliability of Anomaly Detection in Images by Forming Feature Vectors in Wavelet Bases // Informatics and Automation. – 2024. – Vol. 23. – No. 6. – P. 1698–1729. – DOI: 10.15622/ia.23.6.5.
9. Soifer V. A., Fursov V. A., Kharitonov S. I. Kalman Filtering for a Class of Images of Dynamic Objects // Informatics and Automation. – 2024. – Vol. 23. – No. 4. – P. 953–968. – DOI: 10.15622/ia.23.4.1.
10. Verkhotorov A. L., Stepanov A. S., Illarionova L. V. Using Radar Data to Monitor Crop Conditions in the South of the Russian Far East // Informatics and Automation. – 2024. – Vol. 23. – No. 4. – P. 1221–1245. – DOI: 10.15622/ia.23.4.11.
11. Dai M. A Transformer-Based Feature Segmentation and Region Alignment Method for UAV-View Geo-Localization / M. Dai, J. Hu, J. Zhuang, E. Zheng // IEEE Transactions on Circuits and Systems for Video Technology. – 2022. – Vol. 32. – No. 7. – P. 4376–4389.
12. Zuev V. M. Comparison of Object Detection Using Artificial Intelligence vs. Classical Methods // Problems of Artificial Intelligence. – 2024. – No. 3(34). – P. 4–10. – ISSN: 2413-7383. – DOI: 10.24412/2413-7383-2024-3-30-35.
13. Pikalev Y. S. On Neural Architectures for Feature Extraction in Object Recognition Tasks on Devices with Limited Computational Power / Y. S. Pikalev, T. V. Ermolienko // Problems of Artificial Intelligence. – 2023. – No. 3(30). – P. 44–54. – ISSN: 2413-7383. – DOI: 10.34757/2413-7383.2023.30.3.004.
14. Pavlenko B. V. Intellectually Algorithmic Method for Sight Calibration / B. V. Pavlenko, V. I. Bondarenko // Problems of Artificial Intelligence. – 2024. – No. 3(34). – P. 55–63. – ISSN: 2413-7383. – DOI: 10.24412/2413-7383-2024-3-55-63.
15. Krishnan S. R. Improving Anomaly Detection in Video Using Enhanced UNET Technology and Cascade Sliding Window Technique / S. R. Krishnan, P. Amudha // Informatics and Automation. – 2024. – No. 6(23). – P. 1899–1930. – ISSN: 2713-3192. – DOI: 10.15622/ia.23.6.12.
16. Ali B. Navigation Algorithms for Unmanned Aerial Vehicles Using Vision Systems / B. Ali, R. N. Sadekov, V. V. Tsodokova // Gyroscopy and Navigation. – 2022. – Vol. 30. – No. 4(119). – P. 87–105.
17. Khakimov R. S. On the Development of a Data Annotation System for Computer Vision Tasks / R. S. Hakimov, O. L. Nizhnikova, M. V. Blizno // Problems of Artificial Intelligence. – 2024. – No. 3(34). – P. 70–79. – ISSN: 2413-7383. – DOI: 10.24412/2413-7383-2024-3-70-79.
18. Ronzhin A. L. Intellectualization and Robotization of Domestic Scientific Equipment for Interdisciplinary Research / A. L. Ronzhin. – 2023. – Vol. 1. – No. 28. – P. 4–10.
19. Durgama A., Paheding S., Dhiman V., Devabhaktuni V. Cross-view Geo-Localization: A Survey // Ad Hoc Networks. – 2021. – Vol. 29. – No. 3. – P. 1519–1541. – DOI: 10.1016/j.adhoc.2021.101843.
20. Khakimov R. S. Overview of Advanced Augmentation Techniques for Image Datasets / R. S. Khakimov, B. V. Pavlenko, Ya. S. Pikalyov // Donetsk Readings 2024: Education, Science, Innovations, Culture and Challenges of Modernity: Proceedings of the IX International Scientific Conference (Donetsk, October 15–17, 2024). – Vol. 2: Physical, Chemical, Technical, and Computer Sciences. Part 2 / Edited by Prof. S. V. Bespalova. – Donetsk: DonGU Publishing, 2024. – 296 p. – P. 272–275. – ISSN: 2664-7362 (Print); ISSN: 2664-7370 (Online).

Release: 4(35)'2024
Chapter: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
How to quote: Yermolenko T. V. Khakimov R. S. ON THE QUESTION OF APPLICATION OF DEEP LEARNING FOR THE PROBLEM OF CROSS-VIEW GEOLOCALIZATION [Text] / T. V. Yermolenko R. S. Khakimov // Problems of artificial intelligence. - 2024. № 4 (35). - P. 4-15. - http://paijournal.guiaidn.ru/ru/2024/4(35)-1.html

Back to 4(35)'2024