Concept of a geoinformation platform for landmines and other explosive objects detection and mapping with UAV

Mykhailo Popov, Sergey Stankevich, Sergey Mosov, Stanislav Dugin, Stanislav Golubov, Artem Andreiev, Artur Lysenko, Ievgen Saprykin

Abstract


The subject of this article is the concept of a geoinformation platform for landmine detection. Modern warfare and its increasing scale have become a relevant topic today. Undetected explosives threaten business (agriculture, logistics, etc.) and human lives. The problem becomes more acute with the rapid extension of minefield areas, which requires significant time and resources and carries high risks. Remote sensing leverages landmine detection possibilities, providing useful information about landmine displacement with no additional risk during data collection over a large area. This study aims to present a combined approach for revealing hidden landmines using UAVs equipped with different sensor types. The tasks to be solved are to define the overall structure and components of the geoinformation platform, choose the technological solutions for each of them, and implement the system prototype that makes it possible to extend its configuration in the future. The methods used are remote sensing, automated object detection, and centralized data processing in a geographic information system (GIS). Multispectral imagery and magnetometric remote measurements create the background information required to detect landmines and other explosive objects. The results of this study provide a general framework, i.e., a geoinformation platform for landmine detection and mapping. The tasks include UAV-based remote data gathering, UAV mission planning and flight control, data processing and mapping via general GIS, and updating new landmine signatures in the corresponding database. The landmine detection process uses information from the landmine signature database to verify suspicious objects. The results are presented in the form of a probabilistic map, which supports the decision-making process of demining. Conclusion. The proposed approach significantly decreases the time required for landmine detection and mitigates demining risks, which is crucial for dealing with the consequences of war. At present, the concept is being developed in the form of a geoinformation platform research prototype involving an open-source Quantum GIS (QGIS) software system and Python programming language, which is used to create plug-ins for QGIS. The entire landmine remote detection process can be fully automated. Future studies will involve extensive experimental testing and may involve convolutional neural networks (CNN) as a detection mechanism.

Keywords


geoinformation platform; landmine detection and mapping; copter-type UAV; sensor fusion; probabilistic model

Full Text:

PDF

References


Horbulin, V. P. Svitova hlobalʹna problema rozminuvannya: ukrayinsʹkyy vektor [World global demining problem: Ukrainian vector]. Visnyk of the National Academy of Sciences of Ukraine, 2022, no. 2, pp. 3-13. Available at: http://dspace.nbuv.gov.ua/handle/123456789/185010. (accessed Aug. 12, 2024). (In Ukrainian).

Alwatiri, S., Omar, Z., & Algabri, Y. A. Land mines detection, mapping and clearance using Quadcopter in Yemen: a perspective study. Journal of Science and Technology, 2023, vol. 27, no. 2, pp. 32-36. DOI: 10.20428/jst.v27i2.2053.

Schindler, M., & Connell, A. Mine action and food security: the complexities of clearing Ukraine's agricultural lands. The Journal of Conventional Weapons Destruction, 2023, vol. 27, no. 2, pp. 13-24. Available at: https://commons.lib.jmu.edu/cisr-journal/vol27/iss2/3. (accessed Aug. 12, 2024).

Skydan, O., Dankevych, V., Garrett, R. D., & Nimko, O. The state of the agricultural sector in Ukraine during wartime: the case of farmers. Scientific Horizons, 2023, vol. 26, no. 6, pp. 134-145. DOI: 10.48077/scihor6.2023.134.

Bello, R., Literature review on landmines and detection methods. Frontiers in Science, 2013, vol. 3, no. 1, pp. 27-42. Available at: http://article.sapub.org/10.5923.j.fs.20130301.05.html. (accessed Aug. 12, 2024).

Kovács Z., & Ember, I. Landmine detection with drones. Land Forces Academy Review, 2022, vol. 27, no. 1, pp. 84-92. DOI: 10.2478/raft-2022-0012.

Florez-Lozano, J., Caraffini, F., Parra, C., & Gongora, M. Cooperative and distributed decision-making in a multi-agent perception system for improvised land mines detection. Information Fusion, 2020, vol. 64, pp. 32-49. DOI: 10.1016/j.inffus.2020.06.009.

Popov, M. O., Stankevich, S. A., Mosov, S. P., Titarenko, O. V., Topolnytskyi, M. V., & Dugin, S. S. Landmine detection with UAV-based optical data fusion. Proceedings of the 19th International Conference on Smart Technologies (EuroCon 2021). Lviv: IEEE, 2021, pp. 175-178. DOI: 10.1109/EUROCON52738.2021.9535553.

Colorado, J., Mondragon, I., Rodriguez, J., & Castiblanco, C. Geo-mapping and visual stitching to support landmine detection using a low-cost UAV. International Journal of Advanced Robotic Systems, 2015, vol. 12, no. 9. DOI: 10.5772/61236.

Molochko, S. M., Bashynskyj, V. G., Kalamur¬za, O. G., & Zhurakhov, V. A. Analiz suchasnoho stanu, kharakterystyk ta perspektyv rozvytku datchykiv vyyavlennya vybukhonebezpechnykh predmetiv, vstanovlenykh na BpAK [Analysis of the current state, characteristics and prospects of development of explosive ordnance detection sensors mounted on unmanned aerial systems]. Zbirnyk naukovykh pratsʹ Derzhavnoho naukovo-doslidnoho instytutu vyprobuvanʹ i sertyfikatsiyi ozbroyennya ta viysʹkovoyi tekhniky – Scientific works of State Scientific Research Institute of Armament and Military Equipment Testing and Certification, 2021, vol. 8, iss. 2, pp. 80-90. DOI: 10.37701/dndivsovt.8.2021.09. (In Ukrainian).

Kale, M. G., Ratnaparkhe, V. R., & Bhalchandra, A. S. Sensors for landmine detection and techniques: a review. International Journal of Engineering Research & Technology, 2013, vol. 2, no. 1. Available at: http://article.sapub.org/10.5923.j.fs.20130301.05.html. (accessed Aug. 12, 2024).

Nouman, H., Zeeshan, H., Shahzad, A., Shamaraz, F., & Chen, Yi. H. Sensor for landmine detection using unmanned vehicle metal detector and mobile computing technology. Open Access Journal of Environmental and Soil Sciences, 2020, vol. 4, no. 4, pp. 533-540. Available at: https://lupinepublishers.com/environmental-soil-science-journal/pdf/OAJESS.MS.ID.000194.pdf. (accessed Aug. 12, 2024).

Marsh, L.A., van Verre, W., Davidson, J. L., Gao, X., Podd, F. J. W., Daniels, D. J., & Peyton, A. J. Combining electromagnetic spectroscopy and ground-penetrating radar for the detection of anti-personnel landmines. Sensors, 2019, vol. 19, no. 15, article no. 3390. DOI: 10.3390/s19153390.

Popov, M., Stankevich, S., Mosov, S., Saprykin, I. Drone-based landmine detection by image and signal fusion. Proceedings of the TIEMS Hybrid Annual Conference. Port Alfred: TIEMS, 2023, 24 p. Available at: https://www.tiems.info/images/pdf/TIEMS_2023_Hybrid_Annual_Conference_Preliminary_Program_ver_5.pdf. (accessed Aug. 12, 2024).

Vivoli, E., Bertini, M., & Capineri, L. Deep learning-based real-time detection of surface landmines using optical imaging. Remote Sensing, 2024, vol. 16, no. 4, article no. 677. DOI: 10.3390/rs16040677.

Barnawi, A., Kumar, K., Kumar, N., Alzahrani, B., & Almansour, A. A deep learning approach for landmines detection based on airborne magnetometry imaging and edge computing. Computer Modeling in Engineering & Sciences, 2024, vol. 139, no. 2, pp. 2117-2137. DOI: 10.32604/cmes.2023.044184.

Hutsul, T., Khobzei, M., Tkach, V., Krulikovskyi, O., Moisiuk, O., Ivashko, V., & Samila, A. Review of approaches to the use of unmanned aerial vehicles, remote sensing and geographic information systems in humanitarian demining: Ukrainian case. Heliyon, 2024, vol. 10, iss. 7, article no. e29142. DOI: 10.1016/j.heliyon.2024.e29142.

Alegria, A. C., Zimanyi, E., Cornelis, J., & Sahli, H. Hazard mapping of landmines and ERW using geo-spatial techniques. Journal of Remote Sensing & GIS, 2017, vol. 6, iss. 2, article no. 1000197. Available at: https://www.walshmedicalmedia.com/open-access/hazard-mapping-of-landmines-and-erw-using-geospatial-techniques-2469-4134-1000197.pdf. (accessed Aug. 12, 2024).

Rubio, M. D., Zeng, S., Wang, Q., Alvarado, D., Rivera, F. M., Heidari, H., Fang, F. RELand: risk estimation of landmines via interpretable invariant risk minimization. ACM Journal on Computing and Sustainable Societies, 2024, vol. 2, no. 2, article no. 23. DOI: 10.1145/3648437.

Malof, J. M., Morton, K. D., Collins, L. M., & Torrione, P. A. A probabilistic model for designing multimodality landmine detection systems to improve rates of advance. IEEE Transactions on Geoscience and Remote Sensing, 2016, vol. 54, iss. 9, pp. 5258-5270. DOI: 10.1109/TGRS.2016.2559505.

Popov, M. O., Stankevich, S. A., Mosov, S. P., Titarenko, O. V., Dugin, S. S., Golubov, S. I., & Andreiev, A. A. Method for minefields mapping by imagery from unmanned aerial vehicle. Advances in Military Technology, 2022, vol. 17, no. 2, pp. 211-229. DOI: 10.3849/aimt.01722.

Camacho-Sanchez, C., Yie-Pinedo, R., & Galindo, G. Humanitarian demining for the clearance of landmine-affected areas. Socio-Economic Planning Sciences, 2023, vol. 88, article no. 101611. DOI: 10.1016/j.seps.2023.101611.

Rafique, W., Zheng, D., Barras, J., Joglekar, S., & Kosmas, P. Predictive analysis of landmine risk. IEEE Access, 2019, vol. 7, pp. 107259-107269. DOI: 10.1109/ACCESS.2019.2929677.

QGIS. A Free and Open Source Geographic Information System. Official QGIS web site. Available at: https://qgis.org/en/site/ (accessed Aug. 14, 2023).

Flight Planner. QGIS Python Plugins Repository. Available at: https://plugins.qgis.org/plugins/flight_planner/ (accessed Aug. 12, 2024).

Popov, M. O., Stankevich, S. A., Mosov, S. P., Dugin, S. S., & Saprykin, I. Y. Drone-based landmine detection mission planning. Proceedings of 7th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC 2023). Kyiv: IEEE, 2023, pp. 151-154. DOI: 10.1109/MSNMC61017.2023.10329129.

Train AI models in seconds with Ultralytics YOLO. Official Ultralytics Website, Available at: https://www.ultralytics.com/yolo (accessed Aug. 12, 2024).

Saprykin, I. Y. Optical deep learning landmine detection based on limited dataset of aerial imagery. Naukoyemni tekhnolohiyi – Science-based technologies, 2024, vol. 62, iss. 2. DOI: 10.18372/2310-5461.62.18708.

QGIS: Deepness: Deep Neural Remote Sensing. Official Deepness Site. Available at: https://qgis-plugin-deepness.readthedocs.io/en/latest/ (accessed Aug. 12, 2024).




DOI: https://doi.org/10.32620/reks.2024.4.17

Refbacks

  • There are currently no refbacks.