METHODOLOGY FOR CREATING AN AERIAL IMAGE DATASET FOR CROSS-GEOLOCATION TASKS

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

Pavlenko Bogdan Viktorovich
Junior Researcher, Laboratory of Intelligent Systems and Data Analysis.
283048, Donetsk, Artyoma str., 118 b.
Research interests: computer vision, machine learning, neural networks.

Pikalev Yaroslav Sergeevich
Candidate of Technical Sciences, Senior Researcher, Laboratory of Intelligent Systems and Data Analysis.
283085, DNR, Donetsk, Otvazhnykh str., 19, apt. 85, tel. +7 949 428 73-88, email i@pikaliov.ru.
Research interests: digital signal processing, data analysis, pattern recognition, natural language processing, computer vision, machine learning, neural networks.

Download pdf

UDC 004.8, 004.93
DOI 10.24412/2413-7383-2024-4-101-112
Language: Russian
Annotation: Computer vision systems used in unmanned aerial vehicles (UAVs) play a crucial role in a wide range of tasks, including terrain classification, infrastructure monitoring, emergency detection, and identifying transportation and other objects. However, their application in UAV-based computer vision systems requires comprehensive aerial image datasets. The high costs and restrictions associated with aerial data acquisition under certain conditions pose significant challenges for direct data collection. This study presents a methodology for creating a comprehensive and accessible aerial image dataset using third-party cartographic services such as Google Maps, Google Earth, and Google Earth Studio. An algorithm for automated data collection is proposed, which involves dividing the target map area into fixed-size cells and obtaining two types of images: satellite-view and simulated drone-view. The advantages of the proposed automation approach include time and resource efficiency, though it also highlights challenges related to the dynamic nature of web pages and dependence on the functionality of the selected platform.
Keywords: aerial images, computer vision, image recognition, satellite images, 3D visualization, UAVs, cross-view geo-localization.

List of literature:
1. Ronzhin, A.L. Optimizaciya tehnologicheskoj karty dopustimyh sistemotehnicheskih reshenij zadachi videoanalitiki akvakultury [Text] / A.L. Ronzhin, V.N., Le, N.S. Shuvalov // «Bulletin of the South Ural State University». Ser. «Mathematics. Mechanics. Physics». – 2024. – № 2(16). – P. 50-58 – ISSN 2075-809Х. – DOI 10.14529/mmph240205
2. Ronzhin, A. L. Intellektualizaciya i robotizaciya nauchnogo oborudovaniya dlya mezhdisciplinarnyh issledovanij [Text] / A.L. Ronzhin // Problems of Artificial Intelligence. – 2024. – № 1(28). – P. 4-10 – ISSN 2413-7383. – DOI ?????.
3. Verhoturov A.L. Ispolzovanie radiolokacionnyh dannyh dlya monitoringa sostoyaniya posevov selskohozyajstvennyh kultur na yuge Dalnego Vostoka Rossii [Текст] / A.L.Verhoturov, A.S. Stepanov, L.V. Illarionova // Informatics and Automation . – 2024. – № 4(23). – P. 1221-1245 – ISSN 2713-3192. – DOI 10.15622/ia.23.4.11
4. Scientifically Based Forecast for the Development of Precision Agriculture in Russia / E.V. Rudoy, M.S. Petukhova, S.V. Ryumkin, E.V. Truflyak, N.Yu. Kurchenko; Novosibirsk State Agrarian University, Kuban State Agrarian University named after I.T. Trubilin – Novosibirsk: NGAU Publishing Center "Golden Ear", 2021 – 138 pages.
5. Google Earth. [Electronic resource]. Access mode: https://earth.google.co.zm/ (Accessed: 20.10.2024).
6. Google Earth Studio. [Electronic resource]. Access mode: https://earth.google.com/studio/docs/ (Accessed: 20.10.2024).
7. Ali B. Algoritmy navigacii bespilotnyh letatelnyh apparatov s ispolzovaniem sistem tehnicheskogo zreniya [Text] / B. Ali., R.N. Sadekov, V.V. Tsodokova // Gyroscopy and navigation. – 2022. – Vol. 30. – №. 4 (119). – P. 87. – ISSN 0869-7035. – DOI 10.17285/0869-7035.00105
8. Zuev V.M. Sravnenie obnaruzheniya obektov sredstvami iskusstvennogo intellekta v sravnenii s klassicheskimi metodami [Text] / Zuev V.M. // Problems of Artificial Intelligence. – 2024. – № 3(34). –. P. 4-10 – ISSN 2413-7383. – DOI 10.24412/2413-7383-2024-3-30-35.
9. Pikalyov, Ya.S. O nejronnyh arhitekturah izvlecheniya priznakov dlya zadachi raspoznavaniya obektov na ustrojstvah s ogranichennoj vychislitelnoj moshnostyu [Text] / Ya.S. Pikalyov, T.V. Yermolenko // Problems of Artificial Intelligence. – 2023. – № 3(30). –. P. 44-54 – ISSN 2413-7383. – DOI 10.34757/2413-7383.2023.30.3.004
10. Pavlenko, B.V. Intellektualno-algoritmicheskij metod kalibrovki pricelov [Text] / B.V.Pavlenko, V.I. Bondarenko // Problems of Artificial Intelligence. – 2024. – № 3 (34). – P. 55–63. – ISSN 2413-7383. – DOI 10.24412/2413-7383-2024-3-55-63
11. Frolov, V. V. Analysis of the Characteristics of Optical Range Video Cameras for Use in Intelligent Object Detection and Evaluation Systems in Unmanned Aerial Systems [Text] / V.V. Frolov // Artificial Intelligence: Theoretical Aspects, Practical Applications: Materials of the Donetsk International Scientific Round Table. − Donetsk: FSBSI "IPII", 2024. – 328 pages. – P. 304–310.
12. Ulanov, S. I. Methods of Using Unmanned Aerial Vehicles in Flight [Text] / S. I. Ulanov, O. A. Krivodubsky, A. A. Nikitina // Problems of Artificial Intelligence. – 2024. – No. 2 (33). – P. 29–35. – ISSN 2413-7383. – DOI: 10.24412/2413-7383-2024-2-29-35.
13. Durgam A. et al. Cross-view geo-localization: a survey //arXiv preprint arXiv:2406.09722. – 2024.
14. Favorskaya, M.N. Vosstanovlenie aerofotosnimkov sverhvysokogo razresheniya s uchetom semanticheskih osobennostej [Text] / M.N. Favorskaya, A.I. Pahirka // Informatics and Automation. – 2024. – № 4(23). – P. 1047-1076.– ISSN 2713-3192. – DOI 10.15622/ia.23.4.5
15. Zhedong Zheng Yunchao Wei Yi Yang∗ . 2020. University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization. In Proceedings of the 28th ACM International Conference on Multimedia (MM ’20), October 12–16, 2020, Seattle, WA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3394171.3413896
16. Chu M. et al. Towards natural language-guided drones: GeoText-1652 benchmark with spatial relation matching //European Conference on Computer Vision. – Springer, Cham, 2025. – P. 213-231
17. Pikalyov, Ya.S. Razrabotka sistemy normalizacii tekstovyh korpusov [Text] / Ya.S. Pikalyov // Problems of Artificial Intelligence. – 2022. – № 2(25). – P. 64-78. – ISSN 2413-7383. – DOI ?????.
18. Khakimov R.S. K voprosu o razrabotke sistemy annotirovaniya dannyh dlya zadach kompyuternogo zreniya [Text] / R.S. Khakimov, O.L. Nizhnikova, M.V. Blizno // Problems of Artificial Intelligence. – 2024. – № 3 (34). – P. 70–79. – ISSN 2075-1087. – DOI 10.24412/2413-7383-2024-3-70-79
19. Krishnan, Sh. R. Uluchshenie obnaruzheniya anomalij na video s pomoshyu usovershenstvovannoj tehnologii UNET i tehniki kaskadnogo skolzyashego okna [Text] / Sh. R. Krishann, P. Amudha //Informatics and automatisation. – 2024. – № 6 (23). – P. 1899-1930. – ISSN 2713-3192. –DOI 10.15622/ia.23.6.12
20. Shu X, Cheng X, Xu S, Chen Y, Ma T, Zhang W. How to construct low-altitude aerial image datasets for deep learning. Math Biosci Eng. 2021 Jan 5;18(2):986-999. doi: 10.3934/mbe.2021053. PMID: 33757171

Release: 4(35)'2024
Chapter: SYSTEM ANALYSIS, CONTROL AND INFORMATION PROCESSING, STATISTICS
How to quote: Pvlenko B. V. Pikalyov Ya. S. METHODOLOGY FOR CREATING AN AERIAL IMAGE DATASET FOR CROSS-GEOLOCATION TASKS [Text] / B. V. Pavlenko Ya. S. Pikalyov // Problems of artificial intelligence. - 2024. № 4 (35). - P. 101-112. - http://paijournal.guiaidn.ru/ru/2024/3(34)-9.html

Back to 4(35)'2024