Data fusion using aerial photographs and satellite images for detailed landslide assessment
Bibliographic record
Abstract
Hong Kong is one of the world's mountainous international cities, and landslides are a constant threat to human life and property. Monitoring landslides in Hong Kong is important and this is always done by field surveying. However, although conventional survey techniques provide accurate landslide information, they are limited to small areas and physical contact with the slope may be dangerous. Remote sensing techniques can provide an alternative for collecting information about landslide causes and occurrences, and they may assist in the prediction of future landslide occurrences. This article demonstrates the use of monoscopic and stereoscopic aerial photographs, along with satellite images from the IKONOS very high resolution (VHR) sensor for detailed landslide hazard assessment over Hong Kong. For monoscopic aerial photographs, a fusion technique for generating pseudo true colour images from false colour aerial photographs was demonstrated. The pseudo true colour image is useful for better visual analysis in the photogrammetric model. For monoscopic IKONOS image, a set of image fusion techniques was applied in order to improve landslide interpretation, and the results were examined visually and statistically. The Pan-sharpening method among all the image fusion techniques has been demonstrated to have superior performance for identifying both landslide trails and crowns. Stereoscopic viewing using a stereoscopic pair of aerial photographs and stereoscopic IKONOS images was employed for more detailed landslide investigation such as landscape positional relationships (e.g. streams and ridges). Digital elevation models (DEM) were generated from aerial photographs and IKONOS stereoscopic images, and they were compared with digital contour data with 2 m contour interval. The DEMs generated from digital photogrammetric model and IKONOS stereoscopic images are consistently more accurate than an existing DEM, and are sensitive to micro-scale terrain features. The Hong Kong Civil Engineering Department may use the derived monoscopic fused images, stereoscopic images, DEM and anaglyph as objective measures for a detailed landslide study within Hong Kong.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".