Geovisualization of Sub-surface Pipelines: A 3D Approach
Bibliographic record
Abstract
This century has continued to witness an ever increasing reliance on Geographical Information Systems (GIS) technology for the management of utilities’ pipelines world over. Underground cables and pipelines are required to transport essential utilities such as oil, gas, water and electricity from one part of the city to another. Unlike on-surface pipelines, the fact that subsurface pipelines are hidden from the naked eyes makes them susceptible to neglect and damages without being easily noticed. Such damages and consequent pipe failures often have disastrous consequences on the environment and its inhabitants. A common source of subsurface pipeline damage is the accidental cutting of pipelines by excavation workers, oblivious of the precise underground location of such pipelines. This is largely due to the fact that pertinent decisions are usually taken using two dimensional (2D) maps as reference; however, information contained in 2D maps are often misinterpreted by both field workers and professionals alike.Three dimensional (3D) maps are increasingly becoming popular due to their ability to overcome the limitations inherent in (2D) maps. They also aid the proper conceptualization of subsurface pipelines thereby making it easier to work around these pipelines without endangering them. One major drawback though is the exorbitant cost of most of the GIS packages that support the 3D modelling and visualization of subsurface pipelines. Furthermore, the advanced languages used in building many of these packages make it difficult for non-GIS experts and professionals to relate with them. Since people from diverse disciplines (without strong GIS background) need to visualize and analyze these subsurface pipelines on a regular basis, it is pertinent to develop a system capable of performing basic 3D visualization functions, in addition to being user-friendly and highly affordable. This paper discusses such a technique, utilizing open-source software.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".