When we think of archaeology, the image that often comes to mind is of researchers carefully excavating sites and meticulously analysing artifacts. While that's still largely true, a new tool has been added to the archaeologist's kit: machine learning.
Machine Learning: Unearthing Hidden Patterns
Archaeology presents a landscape of abundant and intricate data, ranging from ancient artifacts and site blueprints to satellite images and historical documentation. In this context, the prowess of machine learning algorithms shines bright, adeptly deciphering complex datasets to unearth patterns and foresee outcomes. As a powerful tool, machine learning has become a cornerstone of modern archaeology, empowering researchers to navigate the labyrinth of information with unprecedented precision and efficiency.
However, the transformative journey in archaeology goes beyond the realm of machine learning alone. The true revolution lies in the strategic fusion of machine learning alongside other analytical methods, an approach that expands the horizons of archaeological inquiry and harnesses the potential of an ever-expanding dataset. This profound shift has far-reaching implications, touching both the academic pursuit of knowledge and the realm of cultural resource management. As tools and methodologies evolve in tandem, this paradigm shift redefines how archaeologists perceive and interact with their field.
One of the remarkable achievements of machine learning in archaeology is its prowess in sample identification, unlocking the doors to an ever-growing repository of data. Facilitating the exchange of project data becomes paramount in this context, as it simplifies the identification of novel information and bolsters the reliability of insights. Yet, the true challenge lies in the harmonious integration of these advancements into the fabric of academic and cultural resource management frameworks. This integration demands a new level of competency from archaeologists, who must now navigate and orchestrate a richer, more diverse informational landscape—a task that underscores the pivotal role of expertise in managing and extracting value from this transformative wave of data-driven exploration.
Case Study: Discovering Lost Cities with Machine Learning
Perhaps one of the most exciting applications of machine learning in archaeology is its use in discovering lost cities. Archaeologists and data scientists have trained machine learning models on satellite imagery to identify the subtle marks of human activity, like changes in vegetation or soil colour, that are often invisible to the naked eye. These models can scan vast tracts of land, pinpointing potential sites of interest for further investigation.
One such example is the use of machine learning algorithms to identify potential locations of lost Mayan cities in the dense jungles of Central America. These algorithms have been instrumental in leading researchers to hitherto undiscovered archaeological sites, opening up new avenues in the study of Mayan civilization.
Another study by Bachagha et al. (2023) introduced a comprehensive workflow that integrates SAR and Pleiades imagery with spatial analysis in Google Earth Engine to automate the detection of fortified sites. The random forest-based approach effectively handles data size and structure challenges. The study validates the method's suitability for identifying fortifications in Tunisia and automating their discovery. The algorithm successfully identifies both known and new fortified sites with high precision, utilizing Pleiades and Sentinel data in GEE to enhance its applicability in arid regions and streamline machine learning workflows. However, limitations include restricted availability of Very High-Resolution (VHR) data and associated costs. The research uncovers hidden fortified sites using remote sensing, machine learning, and field investigations, advancing technology and archaeology. The technique's application to a Byzantine fort site demonstrates its ability to reveal historical elements. The study underscores the potential of satellite data and machine learning for uncovering buried archaeological sites, particularly in resource-limited settings. Future improvements may involve convolutional neural networks and Google Earth Engine images to enhance classification accuracy.
Case Study: Restoring Ancient Artifacts with Machine Learning
Machine learning is also being used to restore and interpret ancient artifacts. For example, researchers have used machine learning algorithms to virtually 'unroll' and read fragile ancient scrolls that would crumble if physically handled. In other instances, machine learning has been used to reconstruct broken pottery or even fill in the gaps in ancient texts.
Another example is Qiang Zhao's (2021) research centred on utilizing machine learning algorithms to recognize historical artifacts. The study demonstrated the successful application of decision tree and gradient boosting algorithms in pinpointing cities across China. It also highlighted that a majority of archaeological sites were situated near ancient harbours and in the South China region. The outcomes suggested an impressive 98% accuracy in algorithmic performance. Subsequent investigations could consider incorporating artificial neural networks to forecast potential artifact sites through the analysis of satellite recognition images.
Exploring the Past and Future with Machine Learning
The use of machine learning in archaeology is a striking example of how technology can transform even the most traditional fields. Machine learning is not just about future-forward applications; it can also help us delve into and better understand our past.
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