کاربرد هوش مصنوعی در باستان‌شناسی
علی علی جماعت
1
(
دانشگاه آزاد اسلامی واحد ابهر
)
کلید واژه: باستان‌شناسی, هوش مصنوعی, یادگیری ماشین, یادگیری عمیق,
چکیده مقاله :
دانش باستان‌شناسی برای درک، تجزیه‌وتحلیل تاریخ و فرهنگ‌های انسانی، اهمیت فراوانی دارد. این دانش کمک می‌کند تا گذشته انسان در زمینه‌های مختلف مانند معماری، هنر، اجتماعی، اقتصادی و مذهب به‌درستی مطالعه شود. باستان‌شناسی امکان فهم بهتری از ریشه‌ها، اصالت فرهنگ‌ها و نهادهای را فراهم می‌نماید. تحقیقات در باستان‌شناسی زمینه‌های گوناگونی دارد مانند شناسایی سایت، کاوش سایت‌ها، تحلیل شکل‌ها و خطوط، ترمیم و تجزیه‌وتحلیل بقایای فیزیکی و آثار که همه آن‌ها فرایندی زمان‌بر می‌باشد. باستان‌شناسی به مطالعه گذشته می‌پردازد ولی جامعه باستان شناسان سعی کرده‌اند از فن‌آوری‌های روز مانند هوش مصنوعی در پژوهش‌های خود استفاده نمایند. هوش مصنوعی در سال‌های اخیر به دلیل دسترسی به سخت‌افزارهای توانمند و داده‌های زیاد رشد چشمگیری داشته و در زمینه‌های مختلف باستان‌شناسی مورداستفاده قرارگرفته است. این فن‌آوری درحال ایجاد تحول درروش کشف باستان‌شناسی و تجزیه‌وتحلیل آثار می‌باشد. هوش مصنوعی می‌تواند در شناسایی سایت‌ها، تحلیل محتوا، ترجمه و تفسیر متن‌ها، ترمیم و زمینه‌های دیگر به‌عنوان ابزار کمی به متخصصین کمک نموده و امکان پردازش داده‌های بزرگ که طی دهه‌ها تحقیق گردآوری و ذخیره‌شده را فراهم نماید. در این مقاله کاربرد شاخه‌های مختلف هوش مصنوعی در باستان‌شناسی ارائه‌شده و چالش‌های آن بررسی می‌شود. نتیجه پژوهش حاضر این است که باوجود کاربردهای متعدد هوش مصنوعی در باستان‌شناسی که موجب افزایش سرعت و دقت در کارها می‌شود ولی جایگزین محققان انسانی نشده بلکه به‌عنوان ابزار، توانایی‌های متخصصین را افزایش می‌دهد.
چکیده انگلیسی :
Archaeological knowledge is crucial for understanding, analyzing, and analyzing human history and cultures. This knowledge helps to properly study the human past in various fields such as architecture, art, society, economics, and religion. Archeology provides a better understanding of the roots and authenticity of cultures and institutions. Research in archeology has various fields, such as site identification, site exploration, analysis of shapes and lines, restoration, and analysis of physical remains and works, all of which are a time-consuming process. Archeology deals with the study of the past, but the community of archaeologists has tried to use modern technologies such as artificial intelligence to use your research. Artificial intelligence has grown significantly in recent years due to access to powerful hardware and large data and has been used in various fields of archeology. This technology is creating a transformation in the method of archaeological discovery and analysis of works. Artificial intelligence can help experts in the identification of sites, content analysis, translation and interpretation of texts, restoration, and other fields as a quantitative tool and provide the possibility of processing large data collected and stored over decades of research. In this article, the application of different branches of artificial intelligence in archeology is presented and its challenges are examined. The result of the current study is that despite the numerous applications of artificial intelligence in archeology which increases the speed and accuracy of the works, it does not replace human researchers, but as a tool, it increases the abilities of experts.
Nozari, H., Ghahremani-Nahr, J., Szmelter-Jarosz, A. (2024).AI and machine learning for real-world problems, Adv. Comput. 134, 1–12 Elsevier.
Forte, M., Danelon N. (2019). Cyber-Archaeology. In Oxford Bibliographies in An-thropology, New York: Oxford University Press.
Guyot, A., Lennon, M., Lorho, T.; Hubert-Moy, L. (2021). Combined Detection and Segmentation of Archeological Structures from LiDAR Data Using a Deep Learning Approach. J. Comput. Appl. Archaeol., 4, 1.
Davis, D.S.; Caspari, G. (2021). Lipo, C.P.; Sanger, M.C. Deep Learning Reveals Extent of Archaic Native American Shell-Ring Building Practices. J. Archaeol. Sci. 132, 105433.
Trier,.D.; Reksten, J.H.; Løseth, K. Automated mapping of cultural heritage in Norway from airborne lidar data using faster R-CNN. Int. J. Appl. Earth Obs. Geoinf. 2021, 95, 102241.
Cole, K.E., Yaworsky, P.M., Hart, I.A. (2022). Evaluating statistical models for establishing morphometric taxonomic identifications and a new approach using Random Forest. J. Archaeol. Sci. 143, 105610.
Eberl, M.; Bell, C.S.; Spencer-Smith, J.; Raj, M.; Sarubbi, A.; Johnson, P.S.; Rieth, A.E.; Chaudhry, U.; Aguila, R.E.; McBride, M. (2023). Machine Learning–Based Identification of Lithic Microdebitage. Adv. Archaeol. Pract. 11, 152–163.Electronics 2024, 13, 2752 22 of 23
Troiano, M., Nobile, E., Mangini, F., Mastrogiuseppe, M., Conati Barbaro, C., Frezza, F. (2024). A Comparative Analysis of the Bayesian Regularization and Levenberg–Marquardt Training Algorithms in Neural Networks for Small Datasets: A Metrics Prediction of Neolithic Laminar Artefacts. Information, 15, 270.
Nobile, E., Conati, C.B. (2022). The Standardisation of the PPNB Lithic Industry from Er-Rahib. Orig. Rev. Prehistory Protohistory Anc. Civiliz. 46, 7–28.
Troiano, M., Nobile, E., Grignaffini, F., Mangini, F., Mastrogiuseppe, M., Barbaro, C., Frezza, F. (2024). A Comparative Analysis of Machine Learning Algorithms for Identifying Cultural and Technological Groups in Archaeological Datasets through Clustering Analysis of Homogeneous Data. Electronics. Computer Science & Engineering. 1-23.
Bickler SH. (2019). Machine Learning Arrives in Archaeology. Advances in Archaeological Practice.9(2):186-191. doi:10.1017/aap.2021.6
Bickler, H. (2018). A Machine Learning Identification and Classification of Historic Ceramics.Archaeology in New Zealand.;61(1):20–32.
Chetouani, A., Sylvie, T., Matthieu, E., Sébastien J. (2020). Classification of Engraved Pottery Sherds Mixing Deep-Learning Features by Compact Bilinear Pooling. Pattern Recognition Letters 131:1–7.
Romanengo, C., Silvia B., Bianca S. (2020). Recognising Decorations in Archaeological Finds through the Analysis of Characteristic Curves on 3D Models. Pattern Recognition Letters 131:405–412.
Bonhage, A., Mahmoud E., Thomas R., Michael, B., Anna S. (2021). A Modified Mask Region-Based Convolutional Neural Network Approach for the Automated Detection of Archaeological Sites on High-Resolution Light Detection and Ranging-Derived Digital Elevation Models in the North German Lowland. Archaeological Prospection.DOI:10.1002/arp.1806.
Caspari, G., Pablo, C. (2019). Convolutional Neural Networks for Archaeological Site Detection–Finding “Princely” Tombs. Journal of Archaeological Science 10: 104998.DOI: 10.1016/j.jas.2019.104998.
Davis, D., Katherine, E., Matthew, C. (2020). Addressing the Problem of Disappearing Cultural Landscapes in Archaeological Research Using Multi-Scalar Survey. Journal of Island and Coastal Archaeology, in press. DOI:10.1080/15564894.2020.1803457
Verschoof, V., Wouter, B., Karsten, L. (2019). Learning to Look at LiDAR: The Use of R-CNN in the Automated Detection of Archaeological Objects in LiDAR Data from the Netherlands.Journal of Computer Applications in Archaeology 2:31–40.
Verschoof ,V., Wouter, B., Karsten, L., Wojtek, K., Quentin, P. (2020). Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands. ISPRS International Journal of Geo-Information 9(5):293.
Cintas, C., Manuel L., José M., Claudio D., Rolando, G., Manuel M. (2020). Automatic Feature Extraction and Classification of Iberian Ceramics Based on Deep Convolutional Networks. Journal of Cultural Heritage 41:106–112.
Felicetti, A., Marina P., Primo Z., Roberto P., Eva S. (2021). Mo.Se.: Mosaic Image Segmentation Based on Deep Cascading Learning.Virtual Archaeology Review 12(24):25–38.
Ostertag, C., Marie B. (2020). Matching Ostraca Fragments Using a Siamese Neural Network. Pattern Recognition Letters 131:336–340.
Nash, B.S., Elton R. (2016). The Use of Artificial Neural Networks in Projectile Point Typology. Lithic Technology 41:194–211.
Grove, M., James B. (2020). Neural Networks Differentiate between Middle and Later Stone Age Lithic Assemblages in Eastern.
M. Sakai, Y. Lai, J. O. Canales, M. Hayashi, K. Nomura, (2023). Accelerating the discovery of new Nasca geoglyphs using deep learning. J. Archaeol. Sci. 155, 105777 , DOI: https://doi.org/10.1016/j.jas.2023.105777.
J. Achiam et al., (). Gpt-4 technical report. arXiv [Preprint] (2023). DOI: https://doi.org/10.48550/arXiv.2303.08774
A. Voulodimos, N. Doulamis, A. Doulamis, E. Protopapadakis, (2018). Deep learning for computer vision: A brief review. Comput. Intel. Neurosci., 7068349, DOI: https://doi.org/10.1155/2018/7068349.
Khan, S., Naseer, M, Hayat, M., Hayat, S. Waqas Zamir.Khan, F., Mubarak, S. (2022). Transformers in Vision: A Survey. ACM Comput. Surv. 54, 10s, Article 200, 41 pages. DOI: https://doi.org/10.1145/3505244
Berganzo-Besga, I., Orengo, H., Lumbreras, F., Carrero-Pazos, M., Fonte, J., Vilas-Estévez, B. (2021). Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia. Remote Sensing;13(20):4181. DOI:https://doi.org/10.3390/rs13204181
C. M. Albrecht et al., (2019). “Learning and recognizing archeological features from LiDAR data” in IEEE International Conference on Big Data (2019), pp. 5630–5636, DOI: https://doi.org/10.1109/BigData47090.2019.9005548.
D. S. Davis, G. Caspari, C. P. Lipo, M. C. Sanger, (2021). Deep learning reveals extent of Archaic Native American shell-ring building practices. J. Archaeol. Sci. 132, 105433.
Argyrou, A. Agapiou, (2022). A review of artificial intelligence and remote sensing for archaeological research. Remote Sens. 14, 6000 , DOI :https://doi.org/10.3390/rs14236000.
Sakai M, Sakurai A, Lu S, Olano J, Albrecht CM, Hamann HF, Freitag M. (2024). AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs and sheds light on their purpose. Proc Natl Acad Sci U S A. 2024 Oct;121(40): e2407652121. DOI: 10.1073/pnas.2407652121.
Tsigkas, G., Giorgos S., Anastasios P., Andreas V., Christophoros N. (2020).Markerless Detection of Ancient Rock Carvings in the Wild. Pattern Recognition Letters 135:337–345.
Kogou, S., Golnaz S., Andrei L., Haida L., Biwen S., Wenyuan Z., Bomin S., Sam, v. (2020). From Remote Sensing and Machine Learning to the History of the Silk Road: Large Scale Material Identification on Wall Paintings. Scientific Reports 10:19312. DOI:10.1038/s41598-020-76457-9.
Graham, S., Huffer, D. (2020). Reproducibility, Replicability, and Revisiting the Insta-Dead and the Human Remains Trade. Internet Archaeology. DOI: 10.11141/ia.55.11.
Gutherz, G., Gordin, S., Sáenz, L., Levy O., Berant, J.. (2023). Translating Akkadian to English with neural machine translation. PNAS Nexus.
Sanders H. (2018). Neural Networks, AI, Phone-Based VR, Machine Learning, Computer Vision and the CUNAT Automated Translation App—Not Your Father’s Archaeological Toolkit. In 2018 3rd Digital Heritage International Congress (DigitalHERITAGE) held jointly with 2018 24th International Conference on Virtual Systems & Multimedia, 1–5. DOI:10.1109/DigitalHeritage.2018.8810002.
Bickler, S. H. (2021). Machine Learning Arrives in Archaeology. Advances in Archaeological Practice, 9(2), 186–191. doi:10.1017/aap.2021.6
Tenzer, M., Pistilli, G., Bransden, A., & Shenfield, A. (2024). Debating AI in Archaeology: applications, implications, and ethical considerations. Internet Archaeology, (67).