Developing a hybrid system for sand and dust storm detection using satellite imaging and WSNs
Bibliographic record
Abstract
Sand and dust storms (SDSs) offer very serious hazards to the environment, economy and health. An early warning of the upcoming SDS would allow people to take precautionary measures. Traditionally, satellite imaging is used to detect large-scale and long-term SDSs. However, small-scale and short-term SDSs may go undetected due to the poor spatial and temporal resolution of satellites. We propose a hybrid design of sand and dust storm detection system (SDSDS) using wireless sensor network (WSN) and satellite imaging in order to detect SDSs of all types. A layered architecture of context-aware system is used. While the WSN provides real time data from the area of interest, near-real time METEOSAT MSG images are obtained from their website. An experimental prototype is developed for evaluation of the proposed system. Performance studies show that such a hybrid approach can effectively detect and predict SDSs of all types.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".