TITAN to Google Earth: Workflow for Data Processing for Mobile Terrestrial LIDAR
Bibliographic record
Abstract
Terrestrial LIDAR scanners are pushing the boundaries of accurate urban modelling. Automation and the usability of tools used in feature abstraction and, to a lesser degree, presentation have become the chief concerns with this new technology. To broaden the use and impact of LIDAR in the geomatics, LiDAR datasets must be converted to feature-based representations without loss of precision. One approach, taken here, is to simultaneously examine the overall path that data takes through an organization and the operatordriven tasks carried out on the data as it is transformed from a raw point cloud to final product. We present a review of the current practices in LiDAR data processing and a foundation for future efforts to optimize. We examine alternative LIDAR processing workflows with two key questions in mind: computational efficiency - whether the process can be done using the tools at all - and tool complexity - what operator skill level is needed at each step. Using these workflows the usability of the specific software tools and the required knowledge to effectively carry out the procedures using the tools are examined. Preliminary results have yielded workflows that successfully translate LIDAR to 3D object models, highly decimated point representations of street data represented in Google Earth, and large volume point data flythroughs in ESRI ArcScene. We are documenting the pragmatic limits on each of these workflows and tools for endusers. Terrestrial LIDAR brings with it new innovations for spatial visualizations, but also questions of viability. The technology has proved valuable for specialized applications for experts, but can it be useful as a tool for proliferating 3d spatial information by and to non-experts. This study illustrates the issues associated with preparing 3d LIDAR data for presentation in mainstream visualization environments.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.027 |
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".