The lack of effective methods for rapid and accurate

The gap

Evidence profile

Sourced from the stated research gap and limitations and future-work section of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 5 representative gaps

• Impact of extreme rainfall on triggering conditions and susceptibility for shallow landslides: a case study in the Alpes-Maritimes region (France) (2026) · Natural hazards and earth system sciences · doi

  The outputs and reliability of rainfall-duration thresholds and susceptibility maps can be strongly affected by the representativeness of the landslides included in the inventory. There is a need to investigate the impact of landslides triggered by extreme rainfall events on the determination of rainfall-duration thresholds and susceptibility maps.

  generalstated research gap
  Keywords: outputs reliability rainfall-duration thresholds susceptibility maps strongly affected
• ChangeFormer-Based Detection of Landslide-Damaged Areas Using Sentinel-2 Imagery in South Korea (2026) · Land · doi

  The lack of effective methods for rapid and accurate detection of landslide-damaged areas using satellite imagery and remote sensing data. The need for a robust and generalizable model that can be applied to different regions and environments. The limited availability of reference data for landslide damage in some regions.

  generalstated research gap
  Keywords: lack effective methods rapid accurate detection landslide-damaged areas
• Remote sensing-based landslide prediction and risk assessment using a hybrid CNN–LSTM deep learning model (2026) · Scientific Reports · doi

  Despite its strong performance, several limitations should be noted. First, model accuracy depends on the quality and spatial resolution of remote sensing data, which may be affected by cloud cover, sensor noise, or DEM limitations. Second, the temporal component included only rainfall and reservoir-level fluctuations. The absence of additional time-series variables, such as soil moisture, ground deformation, or seismic activity, may limit representation of all triggering mechanisms. Third, the landslide inventory may be incomplete or biased toward Scientific Reports | (2026) 16:10687 | https://doi.org/10.1038/s41598-026-43927-5 14 www.nature.com/scientificreports/ Fig. 10. Landslide susceptibility maps provided based on deep learning: (a) CNN-only, (b) LSTM-only (the maps were generated using ArcGIS software version 4.10.1, Esri, https://www.esri.com/arcgis).‎ accessible areas, leading to higher uncertainty in regions with sparse records. The framework was evaluated only in Kerman Province, and its transferability to regions with different environmental conditions remains untested. In addition, statistical uncertainty analysis and repeated cross-validation were not performed; therefore, performance differences should be interpreted as indicative rather than statistically conclusive. Finally, random dataset splitting may introduce spatial autocorrelation between subsets, potentially resulting in optimistic estimates. Future studies should apply spatially independent validation strategies, test the framework in multiple regions, and incorporate additional spatiotemporal variables to enhance robustness and generalizability.

  generallimitationsevidence 5/5
  Keywords: regions performance limitations spatial additional variables landslide https maps arcgis esri uncertainty framework validation despite
• Understanding the role of lineament-controlled factors in landslide susceptibility: a machine learning approach in the Uhl Basin, Himachal Pradesh (2026) · Frontiers in Earth Science · doi

  Future studies can focus on the application of machine learning techniques for landslide susceptibility mapping in other regions. Future studies can investigate the use of other machine learning algorithms for landslide susceptibility mapping. Future studies can explore the integration of remote sensing data with machine learning techniques for landslide susceptibility mapping.

  generalfuture-work sectionevidence 5/5
  Keywords: future studies focus application machine learning techniques landslide
• Evaluating model robustness in landslide susceptibility mapping using a unified data-consistent framework in northern Thailand (2026) · Scientific Reports · doi

  The study suggests that future research should focus on developing more robust and reliable models for landslide susceptibility mapping. The study recommends exploring ensemble techniques to improve model stability and reduce prediction errors. The study suggests that future research should investigate the importance of data structure and parameter settings in model selection.

  generalfuture-work sectionevidence 5/5
  Keywords: study suggests future research focus developing robust reliable

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