Abstract: Heritage is seen as a key part of nations, including a broad variety of traditions, cultures, monuments, plants and animals, foods, music, and further. Regarding countries, their own heritages are defined by preservation, excavation, and restoration of historical assets that are important and show the nation's history. It comprises a wide range of physical objects and materials found in cultural institutions which are moveable heritage, as well as the heritage found in built environments which are immovable and natural landscapes. Previous studies on monument classification frequently used single small datasets, limiting accuracy and generalizability. This work introduces a proposed model and a thorough experimental comparison of widely used deep learning architectures, specifically Convolutional Neural Networks and Transformers beside our proposed model, for monument recognition in the cultural monument domain. It seeks to conduct a comparative experiment for selecting representatives from these two methodologies regarding their capacity for transferring information from a general dataset, like ImageNet, to heritage landmarks datasets of varying sizes. When we tested samples of the topologies ResNet, DenseNet, and Swin Transformer (Swin-T), we find that the proposed model had the best results, however ResNet-50 achieved comparable accuracy to Swin-T.

Mennat Allah Hassan, Mona M. Nasr and Alaa Mahmoud Hamdy. “Beyond Ensembles: Architecture-Level Fusion for Enhanced Monument Heritage Recognition”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161167

@article{Hassan2025,
title = {Beyond Ensembles: Architecture-Level Fusion for Enhanced Monument Heritage Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161167},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161167},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Mennat Allah Hassan and Mona M. Nasr and Alaa Mahmoud Hamdy}
}