Digitizing Large Format Aerial Photography Transparencies: Part I
One of the challenges (and rewards) of managing a digital production lab for a university research library is working with the wide assortment of analog formats that are collected within its archives, special collections, and map library holdings. For instance, we've recently begun conversion work on a 2002 aerial survey of Connecticut that was originally shot on 9"x9" positive black and white film.
Aerial photo transparencies are commonly turned into contact prints soon after the film is developed. And indeed, we have a large collection of these prints that we've digitized over the years at UConn. This type of reflective media can be converted in a couple of ways: you can either scan, or digitally photograph the prints at a sufficient spatial resolution. The Federal Agencies Digitization Guidelines Initiative (FADGI) suggest 6,000 pixels across the long dimension of the image area.
When tasked with digitizing original transparencies, however, certain challenges arise. Unlike reflective media, light needs to be evenly shined through transmissives with a light sensing device placed on the opposite side of the illumination source in order to capture an image. Aerial photography service bureaus, for example, employ expensive specialized large format film scanners that can handle the film's actual 10"x10" physical size (both cut and rolled), lighting needs, and high spatial resolution requirements.
As a general rule, photo film contains considerably more visual detail than derivative prints made from film. And indeed, FADGI recommends a considerably greater spatial resolution for the digitization of film vs. reflective prints in this format: 10,000 pixels across the long dimension for aerial transmissives. So, the promise is there for some striking image data if you can engineer a suitable conversion process that is sensitive to both the format's particular handling needs and visually rendered potential.
For my own initial thinking on workflow architecture, the demonstrated design concepts behind both high resolution multi-shot camera backs, and DIY Arduino-controlled film scanners seemed like good theoretical entry points. In addition, I wanted to leverage and re-purpose gear that I already had in the lab. So, I thought, let's start with one of the same light boxes that we use for single-shot medium format film conversion. But instead of using a regular stationary copy stand, let's put the light box on the lab's new X-Y table. Then, let's program the table's movements and camera's controls to create automated, high resolution mosaics of a given 9x9 aerial transmissive. Finally, let's see if the resulting image tiles can be merged into a single, high resolution image for the entire piece of film. If that proves successful, then we'll be able to determine whether or not the image meets FADGI's 10,000 pixel guideline and also better understand the entire workflow's potential for production.
Here's what the concept looked like in staging:
Initial shooting was done with a 50MP Canon 5DsR camera and 100mm macro lens combo. The tandem geometries of the camera's aspect ratio and the 10"x10" (actual size) of the 9x9 format meant that I would make most efficient use of the rig when shooting mosaics in a 2x3 pattern for a total of six images per transparency. Here's a video of the overhead camera's view of the automated system during a shoot in this configuration:
Image tiles were auto-imported directly into Lightroom off of the tethered camera as they were captured. From there, they took a quick trip en masse to Photoshop for final composite image merging. What resulted was an image that was roughly 14,000 pixels across the long dimension, captured as 16 bit data which left plenty of latitude for any needed tone adjustments to more fully express the image's dynamic range. This was encouraging stuff!
In Part II of this post, I'll take a closer look at this file and compare it with other aerial photographs of the same region of Connecticut taken over time with different imaging technologies.