Automated Image Classification for Post-Earthquake Reconnaissance Images
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
In the aftermath of earthquake events, many reconnaissanceteams are dispatched to collect as much data as possible, movingquickly to capture the damages and failures on our built environments before they are recovered. Unfortunately, only a tiny portionof these images are shared, curated, and utilized. There is a pressing need for a viable visual data organizing or categorizing tool witha minimal manual effort. In this study, we aim to build a system toautomate classifying and analyzing a large volume of post-disastervisual data. Our system called Automated Reconnaissance ImageOrganizer (ARIO) is a web-based tool to automatically categorizing reconnaissance images using a deep convolutional neural net-work and generate a summary report combined with useful metadata. Automated classifiers trained using our ground-truth visualdatabase classify images into various categories that are useful toreadily analyze and document reconnaissance images from post-disaster buildings in the field.
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 | 0.001 | 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 | 0.001 |
| 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