Development of a Novel Implementation of a Remotely Piloted Aircraft System over 25 kg for Hyperspectral Payloads
Bibliographic record
Abstract
A main aspect limiting the operation of low-altitude remotely piloted aircraft systems (RPAS) over 25 kg, integrating pushbroom hyperspectral sensors, comes from the challenges related to aircraft performance (e.g., flight time) and regulatory aspects deterring the users from pushing beyond this weight limit. In this study, we showcase a novel implementation using the DJI Agras T30 as an aerial system for integrating an advanced hyperspectral imager (HSI, Hyspex VS-620). We present the design and fabrication approach applied to integrate the HSI payload, the key considerations for powering the HSI and its gimbal, and the results from vibration and wind tunnel tests. We also evaluate the system’s flight capacity and the HSI’s geometric and radiometric data qualities. The final weight of the T30 after the integration of the HSI payload and ancillary hardware was 43 kg. Our vibration test showed that the vibration isolator and the gimbal reduced the vibration transmission to above 15 Hz but also introduced a resonant peak at 9.6 Hz that led to vibration amplification in the low-frequency range near 9.6 Hz (on the order of an RMS of ~0.08 g). The wind tunnel test revealed that the system is stable up to nearly twice the wind speed rating of the manufacturer’s specifications (i.e., 8 m/s). Based on the requirements of the Canadian Special Flight Operations Certificate (RPAS > 25 kg) to land at a minimal battery level of ≥30%, the system was able to cover an area of ~2.25 ha at a speed of 3.7 m/s and an altitude of 100 m above ground level (AGL) in 7 min. The results with the HSI payload at different speeds and altitudes from 50 m to 100 m AGL show hyperspectral imagery with minimal roll–pitch–yaw artefacts prior to geocorrection and consistent spectra when compared to nominal reflectance targets. Finally, we discuss the steps followed to deal with the continuously evolving regulatory framework developed by Transport Canada for systems > 25 kg. Our work advances low-altitude HSI applications and encourages remote sensing scientists to take advantage of national regulatory frameworks, which ultimately improve the overall quality of HSI data and safety of operations with RPAS > 25 kg.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".