Study of landslides caused by the 1999 Chi-Chi earthquake, Taiwan, with multitemporal SPOT images
Bibliographic record
Abstract
This paper presents the results of a study of landslides and other landscape changes caused by the 1999 Taiwan Chi-Chi earthquake using multitemporal Satellite pour l'observation de la terre (SPOT) images. An innovative method for landslide detection is proposed based on the normalized difference vegetation index (NDVI) using image differencing coupled with an automated change threshold calculation. A vegetation recovery index and a land-cover spatial distribution index are also developed to assess vegetation recovery. Landslide extraction by an autodetection technique was also undertaken, yielding Kappa values over 84.16% when compared with those from existing maps. Recovery since the earthquake, despite a typhoon event in 2002, was 66.25% for vegetation in the denuded areas but less on ridge and slope surfaces. The results were verified by field surveys. The methods presented in this paper can be used by government agencies to aid in landslide-area recovery and establish effective land-use policies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".