Performance of automated geoprocessing methods for culvert detection in remote Forest environments
Bibliographic record
Abstract
Greater availability of digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) has made it possible to map precisely hydrographic features such as streams over large watersheds. Road embankments are precisely detected, given that DEMs are especially accurate over open areas, while culverts are not. Consequently, mapped stream positions are often erroneous along and through these anthropogenic structures. The position of actual culverts is often imprecise, incomplete or unavailable for large territories; thus, there is a need to develop and evaluate automated methods to locate culvert positions by remote sensing. Six geoprocessing methods were tested and compared to field-based culvert positioning data gathered in forested areas. These methods rely on preprocessing of depressions, manipulation of road embankment elevation, or both. When exact locations of culverts were unknown, the ‘Breach Depressions’ algorithm (WhiteBox GAT) was most accurate in reducing omission and commission errors. Depending upon the expected stream flow regime, it was possible to reduce cumulative error from 10% to 30% by using this method compared to less effective methods. When exact locations of culverts were known, it was possible to reduce cumulative error from 20% to 45% by burning them into the DEM. Comparisons of two different methods revealed that no automated geoprocessing allowed accurate detection of poorly located culverts, i.e. where small streams deviated into road-side ditches. Despite automated geoprocessing methods that are available, a database geolocating all culverts within a territory is the best way to create exact hydrographic networks without road embankment influence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".