LIDAR Surveys for Road Design in Thailand
Bibliographic record
Abstract
Concerned with environmental and drainage problems associated with road infrastructure that can greatly affect the populations located in its vicinity, the Department of Highways of Thailand (DOH) is looking for ways to minimize the impact of new road construction and maintenance. In particular, roads and highways often need to be built in high-risk and sensitive areas such as hilly, unstable terrain. LiDAR technology is of great interest to DOH as its use can make them save enormous amounts of time and money by providing highly precise elevation data in all kinds of environments including harsh and rugged areas. Generally, survey and design for a common highway requires about 7-8 months for completion while major projects require at least 12 months. Among the many technological improvements introduced over the past years, GIS has been adopted to a certain extent to enhance survey and design capability development. The integration of LiDAR-produced digital elevation models into their existing GIS databases will greatly improve DOH's capacity and efficiency in highway design and associated steps. LaserMap GPR Consultants of Canada is undertaking, together with DOH, Royal Thai Survey Department and Chulalongkorn University, a pilot study located near the New Bangkok International Airport that aims to demonstrate the usefulness and practicality of using this kind of surveys for road construction and maintenance projects in Thailand.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".