Flood Extent Mapping: An Integrated Method Using Deep Learning and Region Growing Using UAV Optical Data
Bibliographic record
Abstract
Flooding occurs frequently and causes loss of lives, and extensive damages to infrastructure and the environment. Accurate and timely mapping of flood extent to ascertain damages is critical and essential for relief activities. Recently, deep-learning-based approaches, including convolutional neural network (CNN) has shown promising results for flood extent mapping. However, these methods cannot extract floods underneath vegetation canopy using the optical imagery. This article attempts to address this problem by introducing an integrated CNN and region growing (RG) method for the mapping of both visible and underneath vegetation flooded areas. The CNN-based classifier is used to extract flooded areas from the optical images, whereas, the RG method is applied to estimate the extent of floods underneath vegetation that are not visible from imagery using the digital elevation model. A data augmentation technique is applied for training the CNN-based classifier to improve the classification results. The results show that the data augmentation can enhance the accuracy of image classification and the proposed integrated method efficiently detects floods in both the visible and the areas covered by vegetation, which is essential to supporting effective flood emergency response and recovery activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".