2026-10-14T04:51:50Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33086412 2026-09-26T18:13:13Z category_146 category_7 category_39 category_734 category_931 portal_87 item_type_3 month_year_09_2026

Metallic landmine detection and identification using machine learning classification of UAV-based magnetometer data Lee-Sun Yoo (24456027) Dahee Jung (3357377) Yosoon Choi (21669768) Space Science Medicine Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Machine learning magnetic anomaly metallic landmines UAV <p>This study presents a UAV-based magnetometer and machine-learning approach for classifying metallic landmines and debris when target locations are unknown. A total of 202 magnetic anomaly signals were extracted from four low-altitude UAV flights over a single test site containing 43 buried metallic landmines and debris. Percentile distance, defined as the distance at which an anomaly decreases to a specified percentage of its maximum, was introduced to characterize signal attenuation. Five machine-learning models were evaluated using maximum anomalies and percentile-distance features. Repeated stratified cross-validation showed that selected percentile distances improved macro-F1 scores for all models compared with the full percentile range. SVM achieved the highest macro-F1 score (0.9586 ± 0.0372), and hold-out testing showed no confusion between landmines and debris. These results support the potential of the proposed features, although validation at additional sites, with more target types and broader UAV survey conditions, is required.</p> 2026-09-26T18:13:13Z Dataset Dataset 10.6084/m9.figshare.33086412.v2 https://figshare.com/articles/dataset/Metallic_landmine_detection_and_identification_using_machine_learning_classification_of_UAV-based_magnetometer_data/33086412 CC BY 4.0