Optical Flow: A Multifaceted Approach for Analyzing and Observing Mass Movements Through Optical and Radar Images
Bibliographic record
Abstract
Abstract Landslides are triggered by various factors, including seismic activity, climate-related events, and gravitational forces. These events pose significant risks to life, property, and the environment, necessitating effective monitoring and quantification for mitigation and prevention. Traditional monitoring methods like in-situ sensors face limitations in cost, scalability, and real-time data processing. In the realm of landslide and hazard mitigation, time is of the essence because the quicker data is processed, the sooner policymakers and emergency responders can act to protect lives and safeguard economic infrastructure. The urgency and the critical role of rapid, real-time data processing have inspired us to expand and further develop a novel open-source package called AkhDefo (Akh: Land in Kurdish language and Defo: Deformation in English Language) ( https://pypi.org/project/akhdefo-functions/ ). This study introduces new features to AkhDefo, transforming it from an open-source code into a standalone geospatial python library. These enhancements include optical flow algorithms for measuring displacement using satellite radar backscatter, optical images, and real-time live stream camera data from ground-based sources. The satellite radar and optical images were processed to derive volume estimates and study kinematic behavior in the May 2017 Mud Creek landslide in California, USA, and the Morenny rock-glacier in the Tien Shan Mountains, Kazakhstan between 2017 to 2023. In addition, live-stream webcam data were used to investigate a rockfall event on the September 20, 2021, at Stawamus Chief in Squamish, British Columbia, Canada, and from this, developed a state-of-the-art rock-fall detection system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".