An integrated convolutional neural network and sorting algorithm for image classification for efficient flood disaster management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 | 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