Survey Data: A Two-Stage Reproducibility Framework for Remote Sensing-Based Landslide Mapping

Landslides are major global hazards that require reliable mapping to help reduce loss of life and economic damage. Rapidly evolving remote sensing methods lack reproducibility assessments, which hinder verification and reuse. Our survey of 134 studies (2020-2025) reveals only 9.0% code and 12.7% data availability, stressing structural barriers necessitating a systematic evaluation approach. To address this, we present a two-stage landslide mapping reproducibility framework: (1) a metadata-based transparency assessment of code, data, and methodology, and (2) an execution-based assessment of data preparation, execution, and output consistency. Applying our framework to selected studies reveals that high transparency does not guarantee successful execution, and the theoretical and practical reproducibility gap must be bridged to improve operational readiness. To establish an empirical baseline, we surveyed a representative set of RS-LSM studies in Scopus to assess code and data availability. We limited the survey to journal articles, and to ensure broad coverage, we used keyword queries targeting a range of methods, from simple change detection to standard machine learning (e.g., RF, SVM) and advanced deep learning (e.g., CNNs, GNNs, Transformers). Each query was sorted by relevance, and up to 20 top-ranked results per query were retained to maintain balance across categories. This strategy provided a representative sample suited to our goal of a preliminary analysis rather than an exhaustive literature review. After merging results, we filtered out duplicates and irrelevant studies lacking RS-LSM methods. Subsequently, we evaluated each study based on the availability of its source code and input data. For both criteria, we marked data or code as unavailable if access required permissions or author contact, as these barriers do not guarantee long-term availability. This dataset includes the queries used for the survey, and resulting list of publications.