Using Citizen Science Data as Pre-Training for Semantic Segmentation of High-Resolution UAV Images for Natural Forests Post-Disturbance Assessment
Bibliographic record
Abstract
The ability to monitor forest areas after disturbances is key to ensure their regrowth. Problematic situations that are detected can then be addressed with targeted regeneration efforts. However, achieving this with automated photo interpretation is problematic, as training such systems requires large amounts of labeled data. To this effect, we leverage citizen science data (iNaturalist) to alleviate this issue. More precisely, we seek to generate pre-training data from a classifier trained on selected exemplars. This is accomplished by using a moving-window approach on carefully gathered low-altitude images with an Unmanned Aerial Vehicle (UAV), WilDReF-Q (Wild Drone Regrowth Forest—Quebec) dataset, to generate high-quality pseudo-labels. To generate accurate pseudo-labels, the predictions of our classifier for each window are integrated using a majority voting approach. Our results indicate that pre-training a semantic segmentation network on over 140,000 auto-labeled images yields an F1 score of 43.74% over 24 different classes, on a separate ground truth dataset. In comparison, using only labeled images yields a score of 32.45%, while fine-tuning the pre-trained network only yields marginal improvements (46.76%). Importantly, we demonstrate that our approach is able to benefit from more unlabeled images, opening the door for learning at scale. We also optimized the hyperparameters for pseudo-labeling, including the number of predictions assigned to each pixel in the majority voting process. Overall, this demonstrates that an auto-labeling approach can greatly reduce the development cost of plant identification in regeneration regions, based on UAV imagery.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".