Assessing Coastal Inundation due to a Transboundary Cyclone Using Sentinel 1 SAR Imagery
Bibliographic record
Abstract
Cyclone Amphan made landfall on the Indian state of West Bengal on May 20, 2020, during the initial phase of the COVID-19 pandemic. It passed through the western edge of the Sundarbans mangrove forest following a north-north-eastern track affecting neighboring Bangladesh as well. The overlapping of this cyclone during the pandemic made emergency response especially challenging for two of the most densely populated countries in the world. Nevertheless, remote sensing has been extremely useful in such scenarios where accessibility and in situ data-sharing are compromised. The application of multispectral satellite imagery is especially common in large-scale impact assessment following natural events, but the presence of clouds during cyclones makes these images often less effective. Sentinel 1 synthetic-aperture radar (SAR) imagery thus can be very useful due to its cloud penetration capability. This study shows that this freely available global data can provide back-of-the-envelope damage assessment using minimal computational facilities. Therefore, the objective is to perform an inundation assessment in coastal districts (level-2 administrative area) of India and Bangladesh due to Cyclone Amphan using Sentinel 1 SAR imagery.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".