Inland and Coastal Hydrographic Feature Identification in the Bahamas Using RADAR Data and Raster Processing in a GIS
Bibliographic record
Abstract
Islands within the Caribbean region are frequented by heavy rains and strong winds, causing flooding and damage to infrastructure and the environment. The increasing availability of spaceborne RADAR data offers advantages over optical imagery for the mapping and mitigation of such hazards. RADAR data has the ability to penetrate cloud cover, making it capable of collecting data during virtually all weather conditions. In this study, the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture RADAR (PALSAR) was used to distinguish seasonally dynamic water bodies on New Providence Island in the Bahamas using an image thresholding technique. The threshold was determined by performing statistics on field-validated training sites. The accuracy of the RADAR data’s classification of water bodies was tested using a control dataset derived from GeoEye-1 imagery and GPS points collected during field work. The RADAR data was found to best classify large, static water bodies. It less accurately classified small, seasonally inundated water bodies and small ponds that are not spatially separated from vegetation. This study demonstrates a practical methodology which can be easily adapted by government and emergency management agencies within the Caribbean, as a preparation and mitigation tool. As such, it addresses the need for accessible data, techniques, and methods, designed to improve the understanding of dynamic natural phenomenon and assist government managers with decision making.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".