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This study presents an unmanned aerial vehicle (UAV)-borne vector magnetometer (MAG) system and proposes a new data-processing technique for modeling the residual magnetic anomalies of three types of landmines: the metallic antitank M15, the metallic antipersonnel M16, and the minimum-metal antitank M19. The burial depth and magnetic moment of these landmines were estimated using the measured and simulated residual magnetic anomalies based on the proposed UAV-borne vector MAG model. Initial in-flight validation showed a strong correlation between the residual magnetic anomaly maps obtained from measurements and simulations. To verify the detection capability in real-world conditions, the UAV-borne MAG system was tested at the Korean Combat Training Center. Both simulations and experiments demonstrated the effectiveness of the proposed data-processing method and UAV-borne MAG model in accurately modeling the residual magnetic anomalies of landmines with metallic components. This approach will facilitate the automated detection of M15, M16, and M19 landmines with high detection rates and enable accurate classification.