National archives worldwide have recently dedicated significant efforts to preserve fragile historical collections (e.g., maps, documents, photographs) through scanning and digitization. Advancements in digital technologies have opened new opportunities for processing and accessing these materials; however, as these collections grow, significant challenges arise in efficiently managing, linking, and using vast volumes of digital data. This paper addresses the challenge imposed by the digital transformation of vast archive collections, with a focus on scanned materials preserved in historical photographic and mapping archives and intended for the creation of digital databases and online catalogues. This study targets the semi-automatic vectorization of aerial photo indexes (APIs), often referred to as finding aids for aerial reconnaissance sorties (i.e., maps on which aerial photo footprints each corresponding to a historical aerial photo from different surveying flight missions were manually drawn onto base topographic maps). Aerial images were captured during the 20th century for military, reconnaissance, and mapping purposes, and their footprints were then manually transposed onto reference maps. Currently, archives and mapping institutions managing creating digital databases and catalogues are manually addressing a demanding and time-consuming vectorization task, and automated solutions are highly needed. This contribution proposes a novel and comprehensive semi-automatic vectorization pipeline that leverages and integrates computer vision techniques and uses deep learning and computational geometry for detecting, isolating, and polygonizing aerial image footprints in APIs. The methodology is applied to a large collection of APIs scanned by the Italian National Historical AirPhoto Archive at the Italian National Institute for Cataloguing and Documentation of the Ministry of Culture and will be added to the online database and WebGIS catalogue recently set up to disseminate copies of historical aerial photographs.
Malek et al. (Thu,) studied this question.