The Forensic Utility of Photogrammetry in Surface Scene Documentation
Bibliographic record
Abstract
Outdoor crime scene documentation needs to be accurate and precise to preserve evidence. Photogrammetry is a potential option. Structure from Motion (SfM) processes photographs into 3D models. As commercial software does not disclose this process, this documentation technique could be legally inappropriate. A potential solution to this problem is open-source software. A series of mock outdoor crime scenes were documented using SfM and total station mapping. Ten large surface scatter scenes containing plastic human remains and personal objects were laid out in 10 × 10 m units in a New England forested environment. The small surface scatter scenes consisted of a pig (Sus scrofa) mandible placed in different environments. The resulting models were built using PhotoScan by AgiSoft and MicMac by IGN. Accuracy was measured by the amount of variance in fixed-datum measurements, whereas visual qualitywas determined by comparison.
 The average total variance in fixed-datum lengths for six of the ten scenes was below 0.635 cm. The maximum differences in measurement between the total station and software measurements were 0.0917 m (PhotoScan) and 0.178 m (MicMac). Comparative histograms had low standard deviations and mean distances between points. Conditions such as light, ground foliage and topography affect model quality. This research shows that SfM has the potential to be a rapid, accurate and low-cost resource, but there are limitations that must be considered.
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".