Mobile augmented reality for flood events management
Bibliographic record
Abstract
The frequency of flood events worldwide has increased significantly over the past decades, and with it so has the need to employ information technologies able to help mobile workforces, both technicians and volunteers, during surveys in the emergency phases. In view of this, a client-server framework for the development of a mobile application that uses Augmented Reality (AR) was implemented. This platform, which increases visual perception of the real world merging additional information with the natural scene in real time, allows mobile workforces to more easily reach the most critical areas subject to flooding and rapidly make a decision on the level of flood protection. The performance of the prototype was evaluated on the Bradano river, located in the south-eastern Basilicata region (Italy), both in a real case study and in a simulated one. The obtained results show how the application represents an innovative tool compared to the existing ones, being it able to show, timely and continuously up-to-date, augmented information on various vulnerability scenarios during the emergency phases, helping both technical and non-technical operators to quickly intervene, containing or preventing secondary disasters, thus reducing deaths and injuries, and limiting the resulting economic losses and social disruption.
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 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 | 0.000 | 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".