Optimal spatial resolution of Unmanned Aerial Vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Species composition is an essential biophysical attribute of vegetative ecosystems. Unmanned aerial vehicle (UAV)-acquired imagery with ultrahigh spatial resolution is a valuable data source for investigating species composition at a fine scale, which is extremely important for species-mixed ecosystems (e.g., grasslands and wetlands). However, the ultrahigh spatial resolution of UAV imagery also poses challenges in species classification since the imagery captures very detailed information of ground features (e.g., gaps, shadow) which would add substantial noise to image classification. In this study, we obtained multi-temporal UAV imagery with 5 cm resolution and resampled them to acquire imagery with 10, 15, and 20 cm resolution. The images were then utilized for species classification using Geographic Object-Based Image Analysis (GEOBIA) aiming to assess the influence of different imagery spatial resolution on the classification accuracy. Results show that the overall classification accuracy of imagery with 5, 10, and 15 cm resolution are close, while the classification accuracy on 20-cm imagery is much lower. These results are expected because the object features (e.g., vegetation index values and standard deviation) of same species vary slightly between 5 and 15 cm resolution, but not at the 20-cm resolution. We also found that the same species show different producer’s and user’s accuracy when using imagery with different spatial resolutions. These results suggest that it is essential to select the optimal spatial resolution of imagery for investigating a vegetative ecosystem of interest.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it