KAUST researchers are using AI to analyze satellite imagery for the automated detection of ancient stone structures in northwest Saudi Arabia, including mustatils (rectangular structures dating to the late 6th millennium BCE) and ruins in circular and triangular shapes. They developed a deep learning algorithm trained on manually identified datasets to isolate similar features over a wide area. The tool converts detected pixels into geodetic coordinates using GPS, assembling them into an online map and database. Why it matters: This project exemplifies computational archaeology, speeding up archaeological discoveries, promoting cultural heritage, and providing a methodology useful to other sectors of the economy.
Dr. Laila Nehme, a French archaeologist from CNRS, visited KAUST as part of the Winter Enrichment Program (WEP) to discuss her work on Mada’in Saleh, also known as Al-Hijr or Hegra. Nehme co-directs the Saudi-French Archaeological Project and specializes in Nabatean epigraphy, studying the daily life of the ancient Nabateans through unearthed remains. Her team, working with the Saudi Commission for Tourism and Antiquities, is beginning its third four-year program to study the site. Why it matters: The research sheds light on the historical significance of Mada’in Saleh, a UNESCO World Heritage Site, and the Nabatean civilization's southernmost settlement, enhancing our understanding of the region's rich cultural heritage.
This article discusses a talk by Gábor Lugosi on "network archaeology," specifically the problems of root finding and broadcasting in large networks. The talk addresses discovering the past of dynamically growing networks when only a present-day snapshot is observed. Lugosi's research interests include machine learning theory, nonparametric statistics, and random structures. Why it matters: Understanding the evolution and origins of networks is crucial for various applications, including analyzing social networks, biological systems, and the spread of information.
Researchers introduce TimeTravel, a benchmark dataset for evaluating large multimodal models (LMMs) on historical and cultural artifacts. The benchmark comprises 10,250 expert-verified samples across 266 cultures and 10 historical regions, designed to assess AI in tasks like classification and interpretation of manuscripts, artworks, inscriptions, and archaeological discoveries. The goal is to establish AI as a reliable partner in preserving cultural heritage and assisting researchers.
A Heritage Commission and KAUST collaboration published in Nature Communications reveals the discovery of large-scale rock art panels in the Nefud Desert, dating back 12,000 years. Over 60 panels with 176 engravings were found depicting animals like camels and ibex. Paleoenvironmental analysis indicates surface water was present 14,000 years ago, supporting early human and wildlife expansion. Why it matters: The findings revise the timeline of human repopulation in Saudi Arabia's interior deserts after the Last Glacial Maximum and demonstrate the significance of interdisciplinary research in understanding the region's climate history.
A collaborative research team including KAUST scientists has located a major sink for missing ocean plastic in coastal sediments and mangrove forests of the Red Sea and Arabian Gulf. Core samples showed a pattern of plastic sedimentation aligning with the history of global plastic production since the 1950s. Mangroves efficiently lock up microplastics in coastal soil, with plastic burial rates increasing similarly to global production. Why it matters: The findings highlight the critical role of mangroves in trapping plastic pollution and provide evidence that plastic sedimentation marks the start of a new geological epoch, the Anthropocene.
A KAUST-led study identified 15 large mammal species that inhabited the Arabian Peninsula in the last 10,000 years, tripling previous estimates. Researchers analyzed thousands of petroglyphs from scientific expeditions, publications, and social media. The study identified two species never known to live in the region before: the greater kudu and the Somali wild ass. Why it matters: The findings provide a benchmark for rewilding efforts and inform decisions on which mammals to reintroduce to the region.
A new framework for constructing confidence sets for causal orderings within structural equation models (SEMs) is presented. It leverages a residual bootstrap procedure to test the goodness-of-fit of causal orderings, quantifying uncertainty in causal discovery. The method is computationally efficient and suitable for medium-sized problems while maintaining theoretical guarantees as the number of variables increases. Why it matters: This offers a new dimension of uncertainty quantification that enhances the robustness and reliability of causal inference in complex systems, but there is no indication of connection to the Middle East.