Wild Ungulate Detections Using RPAS and Satellite Imagery in Manitoba
Bibliographic record
Abstract
Effective wildlife monitoring is essential for the sustainable management of animal populations and their habitats, given the ecological and societal significance of wildlife. Traditional aerial surveys using helicopters and fixed wing aircraft remain the predominant method for tracking wild ungulates. However, they present certain limitations due to their high operational cost, safety risks to crews and animal behavioral impacts. This thesis investigates the potential of remotely piloted aircraft systems (RPAS), commonly known as drones, and satellite imagery as alternative technologies for wildlife monitoring in Manitoba. To evaluate the feasibility of these methods, satellite and RPAS imagery were collected across four Game Hunting Areas (GHAs). GHAs are designated areas used by the province of Manitoba to manage wildlife populations and designate human hunting activities. Satellite imagery was manually reviewed for animal detection, while RPAS imagery was analyzed using thermal thresholding techniques. This study successfully detected farmed cattle in satellite imagery and deer in RPAS imagery. While satellite imagery reliably detected animal groups, RPAS imagery proved more effective overall by enabling the identification of individual animals with greater accuracy and detail. Given the substantial volume of data generated, further advancements in automation for wildlife detection and enumeration are necessary to enhance efficiency and scalability. This research contributes to the ongoing development of innovative monitoring solutions, offering insights into the integration of remote sensing technologies for improved wildlife conservation and management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".