Spatial and temporal evolution of post-disaster data for damage assessment of civil infrastructure systems
Bibliographic record
Abstract
Assessing damage to civil infrastructure is a resource-intensive process that is critical during the response to a disaster. Various datasets facilitate this process but are often collected on an individual ad hoc basis by multiple separate entities. Consequently, there is a lack of a coordinated approach when collecting disaster data, which prevents effective data interoperability. Rather than viewing datasets individually, this paper provides a comprehensive analysis of post-disaster damage data to demonstrate the merits of a dynamic data collection process accounting for both spatial and temporal variations. Specifically, datasets from Hurricane Maria and the Indios Earthquake in Puerto Rico are used to illustrate the entities involved, resources used, and resulting datasets for this purpose. The paper analyzes the evolution of key metadata features as a function of time, including data availability, coverage, and resolution. The results show distinct stages of the data collection process and reveal challenges in collaboration between entities and a lack of data integration for disaster response. The findings also lead to recommendations about the essential metadata for increased shareability. With these outcomes, entities in the field can improve the quality of information extracted and facilitate interoperability and information integration across datasets for damage assessment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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".