Automated first floor height estimation for flood vulnerability analysis using deep learning and Google Street View
Bibliographic record
Abstract
Abstract Flood events can cause extensive damage to physical infrastructure, pose risks to human life, and necessitate the reoccupation and rehabilitation of affected areas. A key parameter for flood vulnerability assessment is the first floor height (FFH), which also plays an important role in setting insurance premiums. Traditional methods for FFH estimation rely on ground surveys and site inspections, yet these approaches are both time‐consuming and labor‐intensive. In this study, we propose an alternative approach based on measurements derived from Google Street View (GSV) images and Deep Learning (DL). We employ the YOLOv5s algorithm, which belongs to a family of compound‐scaled object detection models trained on the COCO dataset, for the detection of crucial building elements such as the Front Door (FD), stairs, and overall building extent. Additionally, we utilized the YOLOv5s algorithm to identify basement windows and assess the existence of basements. To validate our methodology, we conducted tests in both the Greater Toronto Area (GTA) and the state of Virginia in the United States. The results demonstrate an achievement of RMSE and Bias values of 81 cm and −50 cm for GTA, and 95 cm and −20 cm for the Virginia region, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".