Re-engineering the Past: Countrywide Geo-referencing of Archival Aerial Imagery

Abstract

Archival aerial imagery (AAI) represents a unique and relatively unexploited resource for assessing long-term environmental changes at a very high spatial resolution. A major constraint for the wider use of AAI often lies in the difficulties of establishing precise geo-referencing, namely in the difficult and time-consuming task of assigning ground reference through manual digitization of Ground control points (GCPs). We present a highly automated photogrammetric workflow for orientation of AAI. The workflow substitutes manual GCP measurements by generating image matches to a digital reference. The resulting abundant observations are algorithmically filtered and used in a bundle block adjustment (BBA) to obtain final image orientations. The proposed workflow has successfully been employed to process a complete coverage of AAI over the territory of Switzerland based on images acquired between 1985 and 1991. The accuracies obtained from the orientation process are very satisfying and allow for generating meaningful 2D and 3D products. The absolute accuracy for derived orthophotos and their mosaics is about 1 m. The relative accuracies are in the subpixel range and allow for generation of country-wide Digital surface models (DSMs) through dense-image matching. The obtained accuracies are comparable to those obtained at the authors’ affiliation using classical workflows that involve manual GCP identification from digital reference data. With regard to human working time, the workflow has, in our case, proven to be at least five times more efficient than classical workflows whilst the required computational resources are very moderate.

Rethinking Geographic Thought in the Digital Space; A Review of the Arguments for Sustainability and Resilience

“…AI in a Digitized Geographic Era AI has become a signi cant analytical tool in geography due to advancements in computational power, software architectures, sensing hardware, and the availability of big data and volunteered geographic information (Sieber, 2016). Archival aerial imagery (AAI) can be used to assess long-term environmental changes with high spatial resolution, and automated photogrammetric work ows are now available for AAI orientation (Holger & Jean-Luc, 2021).…”