Air photo digitization takes flight: Niagara's journey from paper to points to digital mosaics
Bibliographic record
Abstract
As snapshots in time, historical air photos capture undisputed changes in our landscape. This story begins with a century old air photo, tattered and torn, and begs for preservation. Thousands of air photos later, these have transformed from 9x9 inch contact prints to seamless digital mosaic datasets spanning the Niagara region. Digitization is not only a hot topic for data creation but also an innovative approach to preserving our local heritage resources. Elements of the process include scanning, mosaicking, geo-referencing, and publishing to online mapping environments. Many of these processes foster a collaborative workflow as thousands of traditional resources are scanned for archiving and repurposed into far-reaching digital exhibits. The Niagara Air Photo Index offers a point-and-click environment for researchers to gather information, access individual air photo images, and browse regional mosaics that span 50 years of imagery. What was once a fragile historical document has become a cutting-edge web resource with many stories to tell! This presentation will describe the workflow, processes, and resurfaced stories that this creative initiative has fostered.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".