Detail versus certainty. A metric multi-source reconstruction of the Temple of Bel, Palmyra

Abstract

The Temple of Bel in Palmyra, Syria, was destroyed by insurgents in August 2015 after standing for nearly two millennia. This paper presents a new high-resolution digital reconstruction integrating three heterogeneous datasets: terrestrial laser scanning (TLS) captured by Kiyohide Saito in 2010, spherical panoramic imagery from Gabriele Fangi in 2010, and approximately 1580 crowdsourced tourist photographs spanning 1974–2014 from the #NewPalmyra project. We describe a hierarchical alignment methodology that preserves metric accuracy by sequentially registering data sources in order of decreasing geometric fidelity, and document adaptive reconstruction strategies developed in response to observed artifacts when fusing depth sources of disparate resolution. Across the fusion sequence, texture resolution roughly doubles (optimal texel from 33 to 14 mm exterior, 7.1 to 3.7 mm interior) while geometric agreement with the laser moves the other way: from the 6 mm baseline of simply meshing the laser data on the exterior to roughly 3–4 cm once the crowdsourced frame photography is fused in. The laser-and-panorama core stays within the laser’s detection limit, and the geometric cost arrives only with the crowdsourced frame photography. We further find that internal photogrammetric quality metrics do not predict external accuracy: the lowest-reprojection stage is not the most faithful to the laser, and the panorama stage whose surface best matches the laser carries the highest reprojection of all. All primary datasets are published as independent, citable records and served through a web-based environment for full-resolution comparative analysis without specialized software. The reconstruction is presented not as a finished artifact but as an extensible framework for continued development, in which each dataset, stage, and component remains an independently citable, recomposable layer that future imagery and methods can refine.