Drozd, Sofiia2025-01-022025-01-022024Drozd, 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.https://ela.kpi.ua/handle/123456789/71518This 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.enWar-caused agricultural field damagesSentinel-2 satellite dataRandom Forest-based classificationanomaly detectionArticleP. 701-703https://doi.org/10.1145/3605098.3635169