Mapping of 2021–2022 floods in North America from street photos with artificial intelligence
Bibliographic record
Abstract
Successful flood response and evacuation require timely access to reliable flood depth information in urban areas. However, existing flood-depth-mapping tools do not provide real-time flood depth information in residential areas. In this paper, a deep convolutional neural network is used to determine flood depth through the analysis of crowdsourced images of submerged stop signs. Model performance in pole length estimation is tested on a test set, achieving a root mean squared error of 10.200 in. (1 in. = 1 inch = 2.54 cm) on pre-flood photographs and 6.156 in. on post-flood photographs and an average processing time of 0.05 s. The performance of the developed model is tested on two case studies: Hurricane Ian in the USA (2022) and the Pacific Northwest floods in the USA and Canada (2021), yielding mean absolute errors (MAEs) of 4.375 and 6.978 in., respectively. The overall MAE for both floods is achieved as 5.807 in., which is on par with those from previous studies. Additionally, detected flood depths are compared with readings reported by the nearest flood gauge on the same date. The outcome of this study demonstrates the applicability of this approach to low-cost, accurate, scalable and real-time flood risk mapping in most geographical locations, particularly in places where flood gauge reading is not feasible.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".