Creation and Analysis of Earth’s Surface Roughness Maps from Airborne LiDAR Measurements in Downtown Urban Landscape
Bibliographic record
Abstract
The Earth’s surface roughness constitutes an important parameter in terrain analysis for studying different environmental and engineering problems. Authors gave different definitions and measures for the earth’s surface roughness that usually depend on exploitation of digital elevation data for its reliable determination. This research aimed at exploring the different approaches for defining and extraction of the Earth’s surface roughness from Airborne LiDAR Measurements. It also aimed at evaluating the effects of the window size of the standard deviation filter on the created roughness maps in downtown landscapes using three known approaches namely; standard deviation filtering of the Digital Elevation Model (DEM), standard deviation filtering of the slope gradient model and standard deviation filtering of the profile curvature model. In this context, different roughness maps have been created from Airborne LiDAR measurements of the City of Toronto, Canada using the three filtering approaches with varying window sizes. Visual analysis has shown color tones of small roughness values with smooth textures dominate the roughness maps from small window sizes of the standard deviation filter, however, increasing the window sizes has produced wider variations of the color tones and rougher texture roughness maps. The standard deviations and ranges of the roughness maps from LiDAR DEM have increased due to increasing the filter window size while the skewness and kurtosis have decreased due to increasing the window size, indicating that the roughness maps from larger window sizes are statistically more symmetrical and more consistent. Thus, kurtosis has decreased by 53% and 82% due to increasing the window size to 7 × 7 and 15 × 15 respectively. The standard deviations of the roughness maps from the slope gradient model have increased due to increasing the window size till 15 × 15 while they have decreased with more increases. However, skewness has decreased due to increasing the window size till 15 × 15 and the kurtosis has decreased with higher rate till window size of 11 × 11. In the roughness maps from the profile curvature model, the ranges and skewness have decreased by 93.6% and 82.6% respectively due to increasing the window size to 15 × 15 while, kurtosis has decreased by 58.6%, 76.3% and 93.76% due to increases in the filter window size to 5 × 5, 7 × 7 and 15 × 15 respectively.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".