Enhancement of Urban Floodwater Mapping From Aerial Imagery With Dense Shadows via Semisupervised Learning
Bibliographic record
Abstract
Timely and accurate mapping of floodwater in urban areas from aerial imagery is critical to support emergency response and rescue work. However, massive shadows cast by buildings and trees over dense built-up urban areas can cause a significant underestimation of flood outcomes, and few studies for flood monitoring explore this in current state-of-the-art approaches. Meanwhile, recent deep learning (DL) algorithms have reported superior performance in flood mapping over conventional machine learning methods. Nevertheless, acquiring a large amount of training data remains challenging in the DL paradigm. In this study, to exploit the potential of the DL algorithm in detecting all visible (including shadowed and nonshadowed) floodwater with limited training samples from aerial imagery, we designed a modified fully convolutional network and combined it with a deep semisupervised learning framework integrating consistency regularization and RandMix strategy into the floodwater detection workflow. Besides, the test-time augmentation technique was leveraged to improve the model performance in the evaluation phase. Extensive experiments on the 2013 Calgary flood demonstrated the effectiveness of our approach on extracting visible floodwater in urban areas with dense shadows. Notably, our method could accurately detect more floodwater with a largely reduced number of labeled training samples, which is a considerable enhancement in the applicability and availability of DL algorithms for flood monitoring in densely shadowed urban areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".