Scan Line Intensity-Elevation Ratio (SLIER): An Airborne LiDAR Ratio Index for Automatic Water Surface Mapping
Bibliographic record
Abstract
Owing to the characteristics of how a laser interacts with the water surface and water column, the measured Light Detection and Ranging (LiDAR) intensity values are different with respect to the laser wavelength, the scanning geometry and the reflection mechanism. Depending on the instantaneous water condition and the laser incidence angle, laser dropouts can appear, causing null returns or empty holes found in the collected LiDAR data. This variable intensity response offers a valuable opportunity for using airborne LiDAR sensors for automatic identification of water regions, and thus, we previously proposed an airborne LiDAR-based ratio index named the scan line intensity-elevation ratio (SLIER). Over the water surface, airborne LiDAR data are always found to have a high fluctuation of the intensity value and low variation of the elevation along each scan line, and thus, the water region has a higher SLIER value compared to the land. We examined the SLIER on a multispectral airborne LiDAR dataset collected by Optech Titan and a monochromatic airborne LiDAR dataset collected by Optech Galaxy on a natural rocky shore and a man-made shore. Our experiments showed that SLIER was able to provide a high separability between land and water regions and was able to outperform the traditional normalized difference water index (NDWI) for estimation of the water surface. With the use of SLIER as a mechanism for training data selection, our case studies demonstrated an overall accuracy of 98% in the use of either monochromatic or multispectral LiDAR data, regardless of the laser channel being used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".