Toward the automatic detection of access holes in disaster rubble
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
The collapse of buildings and other structures in heavily populated areas often result in multiple human victims becoming trapped within the resulting rubble. This rubble is often unstable, difficult to traverse and dangerous for first responders who are tasked with finding and extricating victims through access holes in the rubble. Recent work in scene mapping and reconstruction using RGB-D data collected by unmanned aerial vehicles (UAVs) suggest the possibility of automatically identifying potential access holes into the interior of rubble. This capability would allow critical limited search capacity to be concentrated in areas where potential access holes can be verified as useful entry points. In this paper, we present a system to automatically identify access holes in rubble. Our investigation begins with defining a hole in terms of its functionality as a potential means for accessing the interior of rubble. From this definition, we propose a set of discriminative geometric and photometric features to detect “access holes”. We conducted experiments using RGB-D data collected over several disaster training facilities using a UAV. Our empirical evaluation indicates the potential of the proposed approach for successfully identifying access holes in disaster rubble scenes.
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