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.
Edward C. Stone, former Director of the Jet Propulsion Laboratories at Caltech, spoke at KAUST's Winter Enrichment Program in 2017. He discussed the exploration of Neptune's moon Triton, enabled by the Voyager 2 mission. The article includes images from NASA's Voyager 1 and 2 missions and a photo of King Abdullah. Why it matters: Such events and collaborations enhance KAUST's reputation as a leading science and technology university.
This paper surveys machine learning approaches using monument pictures for analyzing heritage sites in India. It addresses challenges in the tourism sector, such as the unavailability of trained personnel and the lack of accurate information. The research aims to provide insights for building an automated decision system to modernize the tourism experience for visitors in India.
This paper introduces a novel approach for monitoring and analyzing the evolution of complex geographic objects in satellite image time-series. The method uses a spatiotemporal graph and constraint satisfaction problems (CSP) to model and analyze object changes. Experiments on real-world satellite images from Saudi Arabian cities demonstrate the effectiveness of the proposed approach.
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.
Ivan Gromicho, a scientific illustrator at KAUST, creates visual representations of complex research findings for journals like Cell, Nature, and Science. He collaborates with KAUST faculty to transform data into comprehensible illustrations. Outside of work, Gromicho pursues rock climbing, exploring natural rock formations across Saudi Arabia. Why it matters: This highlights KAUST's support for interdisciplinary pursuits and employee well-being, fostering innovation at the intersection of science and art.
KAUST researchers used cryogenic electron microscopy (cryo-EM) to study the 3D structure of protein complexes involved in DNA replication and repair. They investigated the interaction between the Y-family TLS polymerase Pol K and mono-ubiquitylated PCNA. The study revealed that DNA binding is required for Pol K to form a rigid, active complex with PCNA. Why it matters: Understanding these structural interactions may provide insights into cancer development and drug resistance mechanisms.