Identifying the Effects of Russian Aggression on Agricultural Fields in Ukraine through Classification Approaches and Satellite Imagery

Myntiuk, Sofiia

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Students Research & Project Works | Роботи студентів
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Факультет прикладних наук
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Бакалаврська програма "Комп'ютерні науки"
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2023
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dc.contributor.author	Myntiuk, Sofiia
dc.date.accessioned	2024-02-14T11:33:29Z
dc.date.available	2024-02-14T11:33:29Z
dc.date.issued	2023
dc.identifier.citation	Myntiuk, Sofiia. Identifying the Effects of Russian Aggression on Agricultural Fields in Ukraine through Classification Approaches and Satellite Imagery / Sofiia Myntiuk; Supervisor: Oles Dobosevych; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2023. – 42 p.: ill.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4427
dc.language.iso	en	uk
dc.title	Identifying the Effects of Russian Aggression on Agricultural Fields in Ukraine through Classification Approaches and Satellite Imagery	uk
dc.type	Preprint	uk
dc.status	Опублікований і розповсюджений раніше	uk
dc.description.abstracten	Detection and assessment of shelling-induced damage to the agricultural fields of Ukraine are crucial for ensuring the safety of civilians, as it helps to estimate the number of unexploded ordnances in the region. Most existing approaches to dam- age detection solve this problem for buildings, and the crater detection task is usu- ally solved either for historical or planetary images. In this thesis, we aim to explore the applicability of existing approaches to the task of crater detection in agricul- tural fields in Ukraine. We collect and annotate a dataset with satellite images of Ukrainian agricultural fields. We experiment with solutions that include classification methods and conduct the hyperparameter search to find the best model for our data. We analyze the impact of each hyperparameter on the network performance and demonstrate the network’s ability to generalize to new locations. The code used in our solution can be found in the project’s GitHub repository.	uk