DEEP LEARNING APLICADO NA DETECÇÃO DE CORPOS D’ÁGUA EM IMAGENS DE VANT DO PANTANAL BRASILEIRO
Bibliographic record
Abstract
The Pantanal, the largest continuous flooded plain in the world, faces preservation challenges due to the seasonal flooding cycle and human interventions. To better understand and preserve this biome, monitoring systems are essential, and the use of remote sensing techniques combined with advanced machine learning emerges as a promising strategy. This study investigated deep learning models for water body segmentation in UAV (unmanned aerial vehicle) images of the Pantanal. The images were captured using the MAVIC 2 Air camera, with a spatial resolution of 3 cm. Deep learning models such as InterImage, DeepLabv3+, and SegFormer were compared to evaluate their segmentation capabilities. A protocol was established for evaluation, considering metrics such as Intersection over Union (IoU) and Dice. SegFormer showed the best results, with an IoU of 96.16%, Recall of 97.85%, Precision of 99.46%, and an F1 Score of 98.04%. Although DeepLabv3+ and InterImage presented lower metrics, they also demonstrated robust performance. All models produced satisfactory results, but some difficulties were observed in accurately identifying water bodies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".