The quality and uncertainty of training samples are a critical bottleneck constraining the performance of machine learning in landslide susceptibility mapping (LSM). To address this, this study introduces a sample purity optimization framework based on state-process consistency (SPC). The framework utilizes two indices, SWCFD and CF-BE-HMD, combined with a mutually exclusive stratified purity strategy (MESPS), to systematicall...