What is a Hole? Discovering Access Holes in Disaster Rubble with Functional and Photometric Attributes
Bibliographic record
Abstract
The collapse of buildings and other structures in heavily populated areas often results in human victims becoming trapped within the resulting rubble. This rubble is often unstable, difficult to traverse, and dangerous for emergency first responders tasked with finding, stabilizing, and extricating entombed or hidden victims through access holes in the rubble. Recent work in scene mapping and reconstruction using photometric color and metric depth (RGB‐D) data collected by unmanned aerial vehicles (UAVs) suggests the possibility of automatically identifying potential access holes into the interior of rubble. This capability would greatly improve search operations by directing the limited human search capacity to areas where access holes might exist. This paper presents a novel approach to automatically identifying access holes in rubble. The investigation begins by defining an access hole in terms that allow for their algorithmic identification as a potential means of accessing the interior of rubble. This definition captures the functional and photometric attributes of holes. From this definition, a set of hole‐related features for detection is presented. Experiments were conducted using RGB‐D data collected over a real‐world disaster training facility using a UAV. Empirical evaluation suggests the efficacy of the proposed approach for successfully identifying potential access holes in disaster rubble.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".