Historical images provide invaluable time series for long‑term environmental monitoring and change analysis, particularly in the context of climate‑change research. Consequently, growing interest in accessing and exploiting archival airborne imagery has been observed worldwide in recent years. In Canada, the federal government manages the National Air Photo Library, which preserves an extensive collection of historical aerial imagery spanning more than a century. Although this archive is highly valuable, historical photographs in their raw form, whether printed or digitally scanned, are not directly suitable for modern data‑driven analytics. Recent advances in computer vision, photogrammetry, and artificial intelligence have created new opportunities to consolidate disparate historical imagery into high‑quality digital map products suitable for large‑scale automated observations and spatio‑temporal analyses. This paper presents solutions applied at Natural Resources Canada for generating analysis‑ready mapping products from the National Air Photo Library, focusing on two main workflows: 1) The photogrammetric processing of historical photos with an emphasis on the more challenging automated georeferencing component; 2) Enhancing interpretability through generative artificial intelligence models for super-resolution and deep colorization.