Optimising camera and flight settings for ultrafine resolution mapping of artificial night-time lights using an unoccupied aerial system
Bibliographic record
Abstract
Light pollution from artificial night-time lights (ANTLs) is a global health, economic, and environmental issue. Remote sensing and unoccupied aerial systems (UASs) provide efficient and cost-effective ways to study ANTL spatial patterns and dynamics over large areas. With ultrahigh-resolution images that can identify individual light sources, UAS offers more detailed image than satellite imagery. However, standardisation and optimisation of camera and flight settings during the acquisition ANTL UAS images is lacking. The aim of this paper is to determine the camera and flight settings to capture high-quality ANTL using a DJI Matrice 300 RTK aircraft with a Zenmuse P1 camera. It emphasises the importance of selecting appropriate camera settings for high-quality ANTL images, which can benefit future research. Results show significant image quality gains when camera and flight settings are chosen appropriately in relation to the lighting conditions. We present three experiments demonstrating a range of camera settings, and we provide practical recommendations for high-quality night-time image collection. The optimal camera settings were determined to be an exposure time of 0.0166 s, ISO of 25600, and aperture of 2.97. This experiment produced outstanding results, with 85% of images having a blur extent below 0.40.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".