An integrated convolutional neural network and sorting algorithm for image classification for efficient flood disaster management
Bibliographic record
Abstract
Drones are used for post-flood disaster management and delivering relief goods to flood-affected areas. Autonomous drones are an alternative means of prioritizing assistance due to the lack of available technology and accessibility in many affected areas during floods. This study proposes a machine-learning approach designed and developed for autonomous drones to identify flood-affected areas with image classification. The proposed integrated approach can be used to deliver relief on a priority basis from the most affected areas to the least affected areas considering distance for efficiency. The proposed system uses a combined convolutional neural network (CNN) and sorting algorithm. The Inception v3 and DenseNet CNN approach can effectively detect flood severity. The Inception v3 shows better performance than DenseNet in terms of image classification. The Inception v3 and DenseNet architectures achieve 83% and 81% accuracy in our self-made flood level dataset, respectively. The integrated algorithm is used to sort the data efficiently. This study demonstrates the efficacy of CNN combined with a sorting algorithm for autonomous decision-making in robotic architecture.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".