Generating Analysis-Ready Geospatial Products from National Historical Air Photos
Bibliographic record
Abstract
The Canadian National Air Photo Library (NAPL) contains millions of historical aerial photographs, spanning over a century, that provide valuable geospatial records of Canada's landscape across both spatial and temporal dimensions.Historical photographs can be utilized to create long-term time series and support various analyses, such as tracking the expansion or contraction of urban areas, measuring changes in forest structure, monitoring the impacts of mine abandonment and reclamation on surrounding environments, assessing the thinning and retreating rates of glaciers, and determining coastal erosion rates.In our presentation, we will discuss the solutions being developed at Natural Resources Canada (NRCan) to produce analysis-ready mapping products from NAPL that include workflows for 1) the photogrammetric processing of historical photos with an emphasis on the more challenging automated georeferencing component and 2) enhancing interpretability through generative artificial intelligence (AI) models for super-resolution and deep colorization, and generating foundational layers (e.g., building outlines) via semantic segmentation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".