Early aerial photography and contributions to Digital Earth - The case of the 1921 Halifax air survey mission in Canada
Bibliographic record
Abstract
This paper presents research into the military and civilian history, technological development, and practical outcomes of aerial photography in Canada immediately after the First World War. The collections of early aerial photography in Canada and elsewhere, as well as the institutional and practical circumstances and arrangements of their creation, represent an important part of remote sensing heritage. It is argued that the digital rendition of the air photos and their representation in mosaic form can make valuable contributions to Digital Earth historic inquiries and mapping exercises today. An episode of one of the first urban surveys, carried out over Halifax, Nova Scotia, in 1921, is highlighted and an air photo mosaic and interpretation key is presented. Using the almost one hundred year old air photos and a digitally re-assembled mosaic of a substantial portion of that collection as a guide, a variety of features unique to the post-war urban landscape of the Halifax peninsula are analysed, illustrated, and compared with records of past and current land use. The pan-chromatic air photo ensemble at a nominal scale of 1:5,000 is placed into the historical context with contemporary thematic maps, recent air photos, and modern satellite imagery. Further research opportunities and applications concerning early Canadian aerial photography are outlined.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".