Editorial: Developments of remote sensing and numerical modeling applications for landslide analysis
Bibliographic record
Abstract
Developments of remote sensing and numerical modeling applications for landslide analysisThe Research Topic "Developments of Remote Sensing and Numerical Modeling Applications for Landslide Analysis" gathers high-quality original research articles on case studies covering a broad spectrum of subaerial landslide types from a wide range of places throughout the world.The common factor of these contributions is the application of state-ofthe-art and new remote sensing techniques, numerical modeling methods, and their combination, for the characterization, monitoring and simulation of the behaviour and the geomorphic evolution of landslides.Where traditional survey methods are used, the Research Topic of geological data required to characterize landslides may be limited or prevented by difficult terrain and the state of activity of the slopes.The use of remote sensing techniques can help overcome these challenges, at the same time allowing monitoring of surface slope displacements and mapping of slope damage features.Multi-sensor, multi-platform, and multi-temporal datasets and approaches maximize the quality and the quantity of remotely sensed data to better characterize the behaviour and the spatial-temporal evolution of landslides.Identification and interpretation of the mechanism and factors controlling the behaviour and the evolution of the landslide can be tested using numerical modelling analyses.Simulations conducted using continuous, discontinuous, and hybrid numerical analyses can be validated and constrained against field and monitoring data, thereby making them an important tool to support and substantiate hypotheses and geological interpretations of the mechanisms and factors influencing the behavior of landslides in both natural and engineered slopes.Addressing these Research Topic requires a multidisciplinary approach encompassing engineering geology, rock and soil mechanics, structural geology, and geomorphology.This Research Topic adds to the state of knowledge of landslides (s.l.) through contributions dealing with case studies of landslide that employ or combine different remote sensing and/or numerical modelling methods to investigate the behavior and evolution of present and past landslides.Research Topic addressed in this Research Topic include the application of state-of-the-art remote sensing techniques for landslide mapping and characterization at various scales (Donati et al., Rouhi et al.Muhammad et al.), landslide susceptibility mapping (Titti et al.), landslide and landslide dam evolution analysis (Bonneau et al., Rabus et al., Wolter et al.), and numerical
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".