Uncertainty Estimation for Deep Learning-Based Segmentation of Roads in Synthetic Aperture Radar Imagery
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Bibliographic record
Abstract
Mission-critical applications that rely on deep learning (DL) for automation suffer because DL models struggle to provide reliable indicators of failure. Reliable failure prediction can greatly improve the efficiency of a system, because it becomes easier to predict when human intervention is required. DL-based systems thus stand to benefit greatly from robust measures of uncertainty over model predictions. Monte Carlo dropout (MCD), a Bayesian method, and deep ensembles (DE) have emerged as two of the most popular and competitive ways to perform uncertainty estimation. Although literature exploring the usefulness of these approaches exists in medical imaging, robotics and autonomous driving domains, it is scarce to non-existent for remote sensing, and in particular, synthetic aperture radar (SAR) applications. To close this gap, we have created a deep learning model for road extraction (hereafter referred to as segmentation) in SAR and use it to compare standard model outputs against the aforementioned most popular methods for uncertainty estimation, MCD and DE. We demonstrate that these methods are not effective as an indicator of segmentation quality when measuring uncertainty (as indicated by model softmax outputs) across an entire image but are effective when uncertainty is measured from the set of road predictions only. Furthermore, we show a marked improvement in the correlation between prediction uncertainty and segmentation quality when we increase the set of road predictions by including predictions with lower softmax scores. We demonstrate the efficacy of our application of MCD and DE methods with an experimental design that measures performance in real-world quality assessment using in-distribution (ID) and out-of-distribution (OOD) data. These results inform the development of mission-critical deep learning systems in remote sensing. Tasks in medical image analysis that have a similar morphology to road structures, such as blood vessel segmentation, can also benefit from our findings.
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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 | 0.000 | 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 | 0.000 | 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