A COMPARISON OF CLOUD REMOVAL METHODS FOR DEFORESTATION MONITORING IN AMAZON RAINFOREST
Bibliographic record
Abstract
Abstract. Deforestation in tropical rainforests is a major source of carbon dioxide emissions, an important driver of climate change. For decades, the Brazilian government has maintained monitoring programs for deforestation detection in the Brazilian Legal Amazon area based on remotely sensed optical images in a protocol that involves considerable efforts of visual interpretation. However, the Amazon region is covered with clouds for most of the year, and deforestation assessment can rely only on images acquired in the dry season when cloud-free images are more likely to capture. One possibility to lessen that restriction and enable deforestation detection throughout the year is to synthesize cloud-free optical images from corresponding SAR images, which are only marginally influenced by atmospheric conditions. This work compares a set of such image synthesis methods, considering deforestation detection in the Amazon forest as the target application. Specifically, we evaluate three deep learning methods for cloud removal in Sentinel-2 images: a conditional Generative Adversarial Network (cGAN) based on the pix2pix architecture; an extension of that method, which uses atrous convolutions (Atrous cGAN) to enhance fine image details; and a non-generative method (DSen2-CR) based on residual networks. In the evaluation, we assess both the quality of the generated images and the accuracy obtained when performing deforestation detection from those images. We further compare those methods with an image aggregation tool available in Google Earth Engine (GEE Tool), which creates cloud-free mosaics from sequences of images acquired at nearby dates. In this study, we considered two sites in the Brazilian Amazon, characterized by distinct vegetation and deforestation patterns. In terms of the quality metrics and classification accuracy, the Atrous cGAN was the best performing deep learning method. The GEE Tool outperformed all those methods when dealing with images from the dry season but turned out to be the poorest performing method in the wet season.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".