Opportunities and challenges provided by regional-scale LiDAR data sets
Bibliographic record
Abstract
Light detection and Ranging (LiDAR) systems are now widely regarded as the preferred source data for a range of terrain mapping applications owing to their high spatial accuracy, dense surface sampling, and the ability of laser scanners to map topography beneath forest and other vegetation covers. In the past, the prohibitive expense and difficulty involved in LiDAR acquisition meant that these data were most often collected in response to project-specific needs and for relatively small spatial extents. In most jurisdictions, the patchwork of LiDAR data that were publicly available were unlikely to allow for regional-scale analyses and applications. However, the decreased cost of acquisition that has occurred over the past decade, and the proliferation of LiDAR data providers, has changed this situation significantly. An increasing number of municipal, regional, provincial and federal governments have been involved in large-scale LiDAR data acquisition campaigns, the result of which has been the availability of regional-scale fine-resolution digital elevation models (DEMs) for use by researchers, practitioners, and other stakeholders. For example, a recent LiDAR acquisition project carried out in Ontario will soon make aerial LiDAR data publicly available in large portions of the province. The recent availability of extensive LiDAR data sets has been marked by a period of exploration, as practitioners work to replace older topographic data with LiDAR and as novel applications of these data emerge. The unique characteristics of these data provide many opportunities to improve existing workflows and processing methods in a wide range of terrain-related fields of study. For example, LiDAR data have been used for soils mapping, forest canopy modelling, stream mapping, sediment erosion modelling, solar potential modelling, and many other applications involving accurate topographic and canopy modelling. However, the properties of LiDAR data also present numerous and significant challenges for end-users and at present practitioners are commonly struggling to take full advantage of their LiDAR data sets. In addition to the technical issues associated with managing large data volumes, researchers and practitioners are also commonly confronted with problems associated with the extremely fine detail of surface representation. For instance, LiDAR DEMs often include microtopography and excessive surface roughness that can complicate the measurement of the surface parameters (e.g. slope, orientation, curvature, topographic position, surface flow) that are common inputs for other upstream modelling workflows. This presentation will introduce potential solutions to some of these issues, as well as describe their role in enabling large-scale applications of these unique data.
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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 | 0.027 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".