War Damage Detection Based on Satellite Data

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dc.contributor.authorShelestov, Andrii
dc.contributor.authorDrozd, Sophia
dc.contributor.authorMikava, Polina
dc.contributor.authorBarabash, Illia
dc.contributor.authorYailymova, Hanna
dc.date.accessioned2023-11-07T13:45:33Z
dc.date.available2023-11-07T13:45:33Z
dc.date.issued2023
dc.description.abstractAs a result of the resolution of the armed military conflict on the territory of Ukraine on February 24, 2022, the agricultural infrastructure of the latter was marked by large-scale destruction. Thousands of hectares of fields, the harvest from which previously provided both domestic and world needs, were mined, destroyed, damaged by artillery shelling, explosions and movements of military equipment. To restore the affected areas to ensure food security of Ukraine and the world, the state government, with the support of international organizations, must correctly distribute financial resources between affected landowners and farmers. For this, there is a need for accurate identification of war-affected territories. This task can be effectively performed using remote sensing data. In this work, damage to agricultural fields due to military operations is searched for by calculating the relative difference of the vegetation indices based on Sentinel-2 satellite data. Cloud-free composites of normalized difference vegetation index (NDVI) are compared for the nearest period before and after active hostilities in a specific area (dates and locations are obtained from the ACLED source). Pixels whose relative difference exceeds a given threshold are considered damaged. The survey of the country's territories was conducted from February 24 to September 25, 2022, dividing the dates into biweekly periods. According to the results of the research, such damage to agricultural fields as craters from explosions and shelling, traces of machinery, burnt fields, etc., were found. The relative difference between the minimum and average values of vegetation indices in the affected areas averaged 25% versus 15% for the minimum period before and after the lesion. The detected damaged areas were validated using ACLED data. It was determined that more than 50% of the total number of areas identified as damaged were located within a radius of up to 5 km from the zone of combat activities.uk
dc.format.pagerangePp. 97-103uk
dc.identifier.citationWar Damage Detection Based on Satellite Data / Andrii Shelestov, Sophia Drozd, Polina Mikava, Illia Barabash, Hanna Yailymova // In Proceedings of International Conference on Applied Innovation in IT , (ICAIIT). – 2023. – Pp. 97-103. – Bibliogr.: 16 ref.uk
dc.identifier.doihttps://doi.org/10.25673/101924
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62047
dc.language.isoenuk
dc.relation.ispartofProceedings of the 11th International Conference on Applied Innovations in IT, (ICAIIT)uk
dc.subjectSentinel-2uk
dc.subjectRelative Difference of NDVIuk
dc.subjectDamaged Agricultural Fields Due to Waruk
dc.subjectValidationuk
dc.subjectACLEDuk
dc.titleWar Damage Detection Based on Satellite Datauk
dc.typeArticleuk

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