THE EUROSDR TIME BENCHMARK FOR HISTORICAL AERIAL IMAGES

Author:

Farella E. M.,Morelli L.,Remondino F.ORCID,Mills J. P.,Haala N.,Crompvoets J.

Abstract

Abstract. Automatic photogrammetric processing of historical (or archival) aerial photos is still a challenging task, particularly in cases of missing ancillary information, low radiometric and image quality, limited stereo coverage or large temporal span. However, with recent advances in photogrammetry and Artificial Intelligence (AI) algorithms for image processing and interpretation, an increasing number of applications are now feasible. The article presents the TIME (hisTorical aerIal iMagEs) benchmark (https://time.fbk.eu/), promoted by EuroSDR to explore the potential of historical aerial images. Realized in collaboration with various European NMCAs, the benchmark has garnered aerial image blocks and time series imagery captured since the 1950s. To support the photogrammetric processing of the digitized photos, ancillary data are supplied with available information about flight missions, taking cameras, and ground control points (GCPs). Several diverse investigations have been undertaken with the benchmark datasets, all captured over historical urban areas or landscapes. The paper describes the benchmark datasets and some potential research topics, presenting several tests and analyses realized with the collated and shared data.

Publisher

Copernicus GmbH

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1. Geoprocessing of archival aerial photos and their scientific applications: A review;Reports on Geodesy and Geoinformatics;2024-07-17

2. The Legacy of Sycamore Gap: The Potential of Photogrammetric AI for Reverse Engineering Lost Heritage with Crowdsourced Data;The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2024-06-11

3. Digitizing Historical Aerial Images: Evaluation of the Effects of Scanning Quality on Aerial Triangulation and Dense Image Matching;Applied Sciences;2024-04-25

4. CHAI: Craters in Historical Aerial Images;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

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