and Remote Sensing A Spatial Filter for the Removal of Striping Artifacts in Digital Elevation Models
Bibliographic record
Abstract
Elongated topographic artifacts, such as the striping produc-tion artifacts described for USGS 7.5-minute DEMs, can result in globally biased estimates of slope and aspect. As such, developing methods to reduce these artifacts and their re-sulting biases is important. This study presents an algorithm for the mitigation of these artifacts, using Terrain Resource Information Management (TRIM) digital elevation models (DEMs) of the Fort St. John Forest District, in British Colum-bia, Canada, as the test bed. The algorithm uses a theoreti-cal error model, where elevation measurement errors are as-sumed to be autocorrelated along the collection lines of the photogrammetric model, and takes advantage of the entry order of DEM points to apply a sequence of spatial filters to the elevation. A probability function is used to constrain the elevation changes to an acceptable range. The algorithm is effective in mitigating the artifacts ’ effects on slope and as-pect while preserving the original topographic detail.
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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 | 0.000 | 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 | 0.000 |
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".