From Film to Data

Abstract

Historical aerial imagery provides valuable data from regions and periods with limited geospatial information. A common method to utilize this data is through the generation of ortho-photos and 3D models using Structure-from-Motion (SfM) techniques. However, many of these images were scanned decades after their acquisition and require geometric calibration, along with internal and external camera parameter estimation, for accurate reconstruction. Manual identification of key features, such as fiducial marks and text annotations, is labour-intensive, while existing automated methods struggle with poor-quality datasets. This paper presents an automated workflow that combines computer vision and machine learning techniques to detect and extract these key features from historical aerial images. To address challenges related to image quality, we also introduce estimation protocols that compensate for missing or unreliable detections by leveraging redundancy across multiple flight paths. The methodology was evaluated on the TMA (Trimetrogon Aerial) archive, a collection of historical images from the Antarctic Peninsula. Our test dataset comprised over 7000 images from 20 different flight paths. The workflow demonstrated high success rates in detecting and extracting fiducial marks, image subsets, and textual annotations. Approximately 70% of the images provided usable focal length data, while fiducial mark detection exhibited high accuracy except in cases of severe scanning artifacts. Altitude data extraction proved to be the most challenging, with successful results in only 15% of images due to degraded altimeter readings. Despite these limitations, the automated workflow effectively estimated missing parameters, ensuring robust image reconstruction across flight paths. The code for this workflow is open-source and publicly available on GitHub at https://github.com/fdahle/hist_meta_extraction.