Bridging land-based mobile mapping using photogrammetric adjustments
Bibliographic record
Abstract
The increasing demand for up-to-date 3-D geographic information systems (GIS) in planning, transportation, and utility management applications poses significant challenges to the Geomatics community. Of all the challenges in acquiring, building, maintaining, and using GIS, none is more central than that of data acquisition. Obtaining the required spatial and attribute data by conventional methods such as aerial photogrammetry and terrestrial surveying is expensive, slow, or inaccurate. These methods are, therefore, not well suited for rapid updating. Fortunately, however, the development of land-based mobile mapping systems (MMS) has opened a new avenue to meet these challenges. Land-based MMS are capable of providing fast, efficient, cost-effective and complete data acquisition. As such, they are an innovative technology for creating and updating 3-D GIS databases both quickly and inexpensively. Basically, MMS are based on integrated navigation systems, mainly the integration of GPS and inertial sensors. The integration of these two technologies results in navigation systems with extremely accurate velocity, position, and attitude with almost no noise or time lags. Under ideal conditions, the GPS measurements are consistent in accuracy and availability throughout the survey mission. However, for land-based MMS, such conditions do not often exist. This is especially prevalent in urban centres and when encountering highway overpasses or tunnels. The overall objective of this paper is investigating a purely photogrammetric strategy for georeferencing of short image sequences as backup system when the navigation solution is not available or not sufficiently accurate, a typical case during GPS signal outage periods. A photogrammetric strategy for the georeferencing of image sequences acquired by MMS is presented. These objectives are accomplished through the simulation of the University of Calgary VISAT
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".