Detection of War-Caused Agricultural Field Damages Using Sentinel-2 Satellite Data with Machine Learning and Anomaly Detection

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dc.contributor.authorDrozd, Sofiia
dc.date.accessioned2025-01-02T13:39:58Z
dc.date.available2025-01-02T13:39:58Z
dc.date.issued2024
dc.description.abstractThis research aims to detect war-caused damages on agricultural fields in Ukraine using Sentinel-2 satellite data. To achieve this, a Random Forest-based classification and an anomaly detection method deployed in the GEE cloud environment are applied. Two spectral bands - blue (B2) and green (B3) and two vegetation indices - NDVI and GCI - were used as input parameters. According to the results, the f1-score of classification reach 0.9. Using the developed methodology, more than 1.5 millions ha of fields were identified as damaged during the period of 2022--2023.
dc.format.pagerangeP. 701-703
dc.identifier.citationDrozd, S. Detection of War-Caused Agricultural Field Damages Using Sentinel-2 Satellite Data with Machine Learning and Anomaly Detection / Sofiia Drozd // SAC ’24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, April 8 –April 12, 2024, Avila, Spain. - Avila, 2024. - P. 701-703.
dc.identifier.doihttps://doi.org/10.1145/3605098.3635169
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/71518
dc.language.isoen
dc.publisherACM Special Interested Group on Applied Computing (SIGAPP)
dc.publisher.placeAvila
dc.relation.ispartofSAC ’24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, April 8 –April 12, 2024, Avila, Spain
dc.subjectWar-caused agricultural field damages
dc.subjectSentinel-2 satellite data
dc.subjectRandom Forest-based classification
dc.subjectanomaly detection
dc.titleDetection of War-Caused Agricultural Field Damages Using Sentinel-2 Satellite Data with Machine Learning and Anomaly Detection
dc.typeArticle

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