SAR and Social-Media-Based Change Detection With Dual-Threshold Fusion for Flood Inundation Mapping
Bibliographic record
Abstract
As one of the most destructive natural disasters, floods are increasingly frequent and severe due to urban development and population growth. The threshold-based method is widely acknowledged as an effective approach for detecting flood extent in synthetic aperture radar (SAR) imagery. However, determining the accurate threshold value poses a significant challenge. During periods of flooding, social media (SM) data posted by users provide a wealth of real-time information for flood inundation mapping (FIM) purposes. This study presents a new semi-automatic threshold determination method called SAR and social media-based dual threshold (SSM-DT) for FIM. SSM-DT aims to improve accuracy by avoiding traditional method inaccuracies through a semi-automatic approach. Integration of SM data enhances real-time flood situation monitoring, enriching FIM comprehensiveness. Firstly, the SAR images are processed and analyzed using change detection techniques to identify potential flood inundation areas. Simultaneously, a deep learning model is utilized to classify and filter SM data, enabling the retrieval of real-time flood-related information. Finally, the flood information obtained from both SAR images and SM data is fused together to generate a more accurate and comprehensive flood inundation map, leveraging the complementary nature of these two data sources. A case study focused on the extensive flooding caused by Hurricane Harvey in Houston in 2017 is discussed. The results demonstrate that the proposed method can provide near real-time depiction of flood extent, which is crucial for mitigating economic losses and minimizing casualties.
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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 | 0.000 | 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 | 0.000 | 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".