Application of RPAS to disaster risk reduction in Brazil: application in the analysis of urban floods
Bibliographic record
Abstract
Geotechnologies have significant potential for application in socio-environmental analysis coupled to disaster risk reduction. Equipment and applications are available that are supported by scientific computing, promoting advances in the acquisition and processing of remote sensing data. Among these are two types: (i) the associated equipment to technology LiDAR (light detection and ranging) and (ii) remotely piloted aircraft systems (RPAS) with platforms of remote sensors. Recently, an growing number of studies has been observed that have the potential for applications in the sensors equipped in RPAS for environmental studies, especially those that evaluate the impacts of natural disasters. In this context, the aim of this research is to demonstrate the possibilities of RPAS applications in the collection of data of interest in the management of natural disasters. Also associated with this task is the prospect of implementing some techniques of scientific computing necessary for the implementation of applications. With these activities, we seek to contribute to the advancement of the employment of RPAS in managing and preventing the risk of natural disasters.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".