Rapid Damage Estimation of Texas Winter Storm Uri from Social Media Using Deep Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
The winter storm Uri that occurred in February 2021 affected many regions in Canada, the United States, and Mexico. The State of Texas was severely impacted due to the failure in the electricity supply infrastructure compounded by its limited connectivity to other grid systems in the United States. The georeferenced estimation of the storm’s impact is crucial for response and recovery. However, such information was not available until several months afterward, mainly due to the time-consuming and costly assessment processes. The latency to provide timely information particularly impacted people in the economically disadvantaged communities, who lack resources to ameliorate the impact of the storm. This work explores the potential for disaster impact estimation based on the analysis of instant social media content, which can provide actionable information to assist first responders, volunteers, governments, and the general public. In our prototype, a deep neural network (DNN) uses geolocated social media content (texts, images, and videos) to provide monetary assessments of the damage at zip code level caused by Uri, achieving up to 70% accuracy. In addition, the performance analysis across geographical regions shows that the fully trained model is able to estimate the damage for economically disadvantaged regions, such as West Texas. Our methods have the potential to promote social equity by guiding the deployment or recovery resources to the regions where it is needed based on damage assessment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it