Co-Seismic Landslide Detection Combining Multiple Classifiers Based on Weighted Voting: A Case Study of the Jiuzhaigou Earthquake in 2017

Co-seismic landslides are major secondary hazards in earthquakes, and their rapid detection is essential for emergency response, disaster assessment, and post-earthquake reconstruction. However, single classifiers often fail to meet practical detection requirements. This study proposes WPU, a weighted-voting-based multi-classifier method that assigns category-specific weights using the producer’s accuracy and user’s accuracy. A case study was conducted in Jiuzhaigou County, Sichuan Province, China, affected by the Ms 7.0 earthquake on 8 August 2017. A dataset of 193 co-seismic landslides was built through manual interpretation, and six commonly used remote-sensing-based detection methods were employed. The WPU method fused the outputs of all classifiers using PA- and UA-based weights. Results show that WPU achieved an overall accuracy of 0.9755 and a Kappa coefficient of 0.7848, demonstrating substantial improvement over individual classifiers while maintaining efficiency and timeliness. The proposed approach supports rapid emergency assessment and enhances the effectiveness of co-seismic landslide detection, providing a valuable reference for future post-earthquake hazard evaluations and enabling governments to respond more quickly to landslide disasters.