Estimating lighting device inventories with the LANcube v2 multiangular radiometer
Bibliographic record
Abstract
This paper is a technical report describing the use of an open source instrument called LANcube v2 to estimate a lighting devices inventory. The LANcube v2 is and instrument having 5 color sensitive sensors, each on a face of a cube. The instrument can be mounted on a car roof in order to create a map of the artificial light at night while roaming the streets and roads. Based on the temporal variations of the detected signal on various cube’s faces, we developed a method of finding the approximate position in 3D of each source. The lamp spectral types can be determined relatively well thanks to the color balance of the raw Red (R), Green (G), Blue (B), and Clear (C) color bands. If one assumes a typical angular photometry of a source with respect to its location, it is possible to estimate roughly its current luminous flux. Such information allows us to build a lighting devices inventory of a territory. One advantage of that new method is that it can provide information about the private sources that are always excluded from public lighting inventories. We compared the inventory extracted with that new methodology with an in situ lamp inventory made for two villages in Canada. This comparison allow us to emphasize the strengths and limitations of the method by comparing to the ground truth. We actually found that we were able to detect 99% of the sources with flux higher than 1000 lumen and located within 15 meters from the road. We also found that we generally overestimate the height of the devices by 21 to 51% depending on the lamp photometry. This overestimate surely reflect in an overestimate of the fluxes. Finally, we found that the proposed method is very efficient to recognize the spectral type of the devices with 99% of success.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".