Appearance Meets Geometry: Deep Learning for Semantic Segmentation of Archaeological Fortification Masonry

This is the preprint for a conference proceedings article to be submitted to the CAA 2025 proceedings. The title of the presentation held by Nils Schnorr during the 2025 conference was "Machine Learning in Archaeological Fortification Studies – A CNN-Based Approach for the Caria Region"

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-23
DOI
https://doi.org/10.5281/zenodo.21507975
Primary Topic
Image Processing and 3D Reconstruction
Type
preprint
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preprint

Appearance Meets Geometry: Deep Learning for Semantic Segmentation of Archaeological Fortification Masonry

Thomas Leimkühler, Nils Schnorr
Zenodo (CERN European Organization for Nuclear Research)
Image Processing and 3D Reconstruction
preprint

Appearance Meets Geometry: Deep Learning for Semantic Segmentation of Archaeological Fortification Masonry

Thomas Leimkühler, Nils Schnorr
preprint en

Abstract

This is the preprint for a conference proceedings article to be submitted to the CAA 2025 proceedings. The title of the presentation held by Nils Schnorr during the 2025 conference was "Machine Learning in Archaeological Fortification Studies – A CNN-Based Approach for the Caria Region"

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Image Processing and 3D Reconstruction
DOIOpen Access PDFOpenAlex
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