ARVEE: AUTOMATIC ROAD GEOMETRY EXTRACTION SYSTEM FOR MOBILE MAPPING
Bibliographic record
Abstract
Land-based mobile mapping systems have yielded an enormous time saving in road networks and their surrounding utilities survey. However, the manual extraction of the road information from the mobile mapping data is still a time-consuming task. This paper presents ARVEE (Automated Road Geometry Vectors Extraction Engine)), a robust automatic road geometry extraction system developed at the University of Calgary. The extracted road information includes the 3D lane lines, road edges as well as lane lines attributes. There are three innovations in this work. First, all the visible lane lines in the georeferenced image sequences are extracted, instead of only extracting the central lane line or the nearby lane line pair. Second, the lane line types are recognized, so the output is a functional description of the road geometry. Third, the output is the absolute-georeferenced model of lane lines in mapping coordinates, and is compatible to the GIS databases. Four steps are included: First, extract the linear features in each image. Second, the linear features are filtered and grouped into lane line segments (LLS). Geometric and radiometric characteristics are extracted for each LLS. Third, a Multiple-Hypothesis Analysis (MHA) method is used to link the LLSs into long lane lines 3D model. Finally, each lane line is classified into a lane line type based on the synthetic analysis of the included LLSs ’ features. The system has been tested on large number of VISAT ™ mobile mapping data. The experiments on massive real MMS data sets demonstrate that ARVEE can deliver accurate and robust 3D continuous functional road geometry model. Full automatic processing result from ARVEE can replace most of the human efforts in road geometry modelling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".