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The Technology Innovation Institute Develops New Drone Technology to Detect Hidden Water Leaks from the Sky

TII ·

Technology Innovation Institute (TII) has developed a drone-based Synthetic Aperture Radar (SAR) system capable of detecting underground water leaks at depths of up to 40 meters. The system uses P-, L-, and C-band radar signals to identify anomalies in soil moisture and subsurface disturbances. The SAR technology was previously validated for archaeology and infrastructure and is now optimized for sandy environments. Why it matters: This innovation offers a more efficient and sustainable method for monitoring infrastructure, reducing water loss and maintenance costs for utilities across the region.

Rock art shows earliest known humans returned to Arabia after the last Ice Age

KAUST ·

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.

Utilizing artificial intelligence to uncover the Kingdom’s ancient stone structures

KAUST ·

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.

KAUST alum Hanin Ahmed awarded prestigious Marie Curie Postdoctoral Fellowship

KAUST ·

KAUST alumna Dr. Hanin Ahmed has been awarded a Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship to research the biological traits, ancestry, and symbolic roles of horses used in ancient rituals. She will analyze DNA samples from 97 sites across France, linking biology with ritual behavior. Ahmed previously held an Ibn Rushd Fellowship from KAUST, which supported her move to the University of Toulouse. Why it matters: This prestigious fellowship highlights the quality of research and training at KAUST while enabling exploration of the co-evolution of humans and animals through genomics and archaeology.

WEP 2015: Unearthing the history of Mada’in Saleh

KAUST ·

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.

Problems in network archaeology: root finding and broadcasting

MBZUAI ·

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.