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Lookup NU author(s): Alex Turner, Professor Jon MillsORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© Author(s) 2024.The orientation of crowdsourced and multi-temporal image datasets presents a challenging task for traditional photogrammetry. Indeed, traditional image matching approaches often struggle to find accurate and reliable tie points in images that appear significantly different from one another. In this paper, in order to preserve the memory of the Sycamore Gap tree, a symbol of Hadrian's Wall that was felled in an act of vandalism in September 2023, deep-learning-based features trained specifically on challenging image datasets were employed to overcome limitations of traditional matching approaches. We demonstrate how unordered crowdsourced images and UAV videos can be oriented and used for 3D reconstruction purposes, together with a recently acquired terrestrial laser scanner point cloud for scaling and referencing. This allows the memory of the Sycamore Gap tree to live on and exhibits the potential of photogrammetric AI (Artificial Intelligence) for reverse engineering lost heritage.
Author(s): Morelli L, Mazzacca G, Trybala P, Gaspari F, Ioli F, Ma Z, Remondino F, Challis K, Poad A, Turner A, Mills JP
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: ISPRS TC II Mid-term Symposium “The Role of Photogrammetry for a Sustainable World”
Year of Conference: 2024
Pages: 281-288
Online publication date: 11/06/2024
Acceptance date: 02/04/2024
Date deposited: 16/07/2024
ISSN: 2194-9034
Publisher: Copernicus GmbH
URL: https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-281-2024
DOI: 10.5194/isprs-archives-XLVIII-2-2024-281-2024
Series Title: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
