Artificial intelligence for rapid mapping of potential archaeological features using bag of visual words based image classifier

• Main Authors: Roslan, Shairatul Akma, Abd Rahim, Muhamad Sharifuddin, Yakub, Fitri, Kong, Yong Chee, Hj. Mohd. Noor, Norzailawati
• Format: Proceeding Paper
• Language: English; English
• Published: IOP Publishing Ltd. 2024
• Subjects: G Geography (General); GE Environmental Sciences; H Social Sciences (General); T Technology (General)
• Online Access: http://irep.iium.edu.my/117309/7/117309_Artificial%20intelligence%20for%20rapid.pdf; http://irep.iium.edu.my/117309/8/117309_Artificial%20intelligence%20for%20rapid_Scopus.pdf; http://irep.iium.edu.my/117309/; https://iopscience.iop.org/article/10.1088/1755-1315/1412/1/012030
• Institution: Universiti Islam Antarabangsa Malaysia

Integrating Artificial Intelligence technological advancements in archaeology has revolutionised automated feature detection, presenting a novel perspective on archaeological feature recognition and image interpretation. This approach reduces costs associated with ground data collection and enhances the reliability and productivity of large-scale archaeological mapping. Consequently, this study aims to explore feature detection and matching techniques in archaeological detection using Artificial Intelligence and Scale-Invariant Feature Transform and Oriented Fast and Rotated Brief algorithms, which are frequently employed in image processing applications as a feature descriptor within the Bag-of-Visual-Words framework. The highresolution multispectral satellite SPOT image maps potentially hidden archaeological features in Bujang Valley, Kedah, Malaysia. The expected outcome involves presenting a BoVW model capable of accurately detecting hidden archaeological features within the generated maps, thereby providing valuable insights into the extent and distribution of archaeological remnants in the targeted regions.