Building Damage Detection in Post-Event High-Resolution Imagery Using Deep Transfer Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the most important disaster management requirements is accurate damage map generation to support rescue and reconstruction efforts. In this application, remote sensing images play a significant role because of the great details provided by their high spatial, spectral, and temporal resolutions; thus, the literature is rich with studies that use pre- and post-event images along with geospatial machine learning techniques for automatic damage mapping. However, acquiring proper pre-event data can be challenging due to the unpredictable nature of hazards. In this paper, we customize a pre-trained version of the residual neural network with 34 layers (ResNet-34) to identify damaged buildings by using only post-event high-resolution remote sensing images. For evaluating the damage detection framework efficiency, airborne orthophotos of the 2010 Haiti earthquake and the 2018 Woolsey fire are utilized. The network identified damaged and non-damaged buildings with over 91 % overall accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it