Study Finds Island Once Held a Larger Ancient Population听
Researchers at Khalifa University鈥檚 Environment and Geophysical Sciences () Lab have combined artificial intelligence, satellite radar imaging and ground-penetrating radar surveys to identify previously undocumented subsurface features on Siniyah Island in Umm Al Quwain, demonstrating a non-invasive approach that could help archaeologists investigate heritage sites across the UAE.听
The team applied the methodology to the whole Siniyah Island to identify from space potential areas where archaeological remains are still buried. Seven distinct and previously unknown areas were found. For the validation phase via geophysical surveys, three priority zones surrounding Siniyah Island’s sixth-century Christian monastery, one of the UAE’s most significant archaeological discoveries, were selected to validate the findings from satellite-AI analysis and to construct a 3D model of the subsurface features that can be used by archaeologists to guide excavation with no damage to the remains.听听
The findings were in the International Journal of Applied Earth Observation and Geoinformation by Dr. Diana Francis, Head of ENGEOS Lab, and researchers Sonia Santos-Assun莽茫o, Charfeddine Cherif, Moamen Ali and Mohammed Y. Ali from the ENGEOS Lab and the Department of Earth Sciences.听
The surveys revealed numerous subsurface anomalies and structural patterns consistent with archaeological features, including rectangular formations and wall-like structures. Two notable features detected in one survey area closely matched structures visible in historical satellite imagery, despite having become reburied by sand by the time field surveys were conducted.听
The number and spread of anomalies found across all three zones suggest Siniyah Island once supported more people than the structures already excavated and mapped would indicate. Beyond the archaeological findings themselves, the study highlights the potential of advanced geospatial-AI technologies to support heritage preservation and guide future investigations across the UAE and other arid environments.听
The work also demonstrated that the methodology remained effective in challenging environmental conditions. Siniyah Island鈥檚 low elevation, salt intrusion and sabkha soils can significantly weaken radar signals and complicate subsurface investigations. Despite these conditions, the team successfully mapped buried features and generated three-dimensional visualizations of potential structures.听
The team used a similar satellite-to-ground approach before, at the Saruq Al-Hadid site in Dubai, where it helped confirm more than 400 subsurface anomalies linked to suspected Iron Age and Wadi Suq-era occupation.听
Siniyah Island is home to one of the UAE鈥檚 most significant archaeological sites, including the ancient Christian monastery and evidence of early settlements. Researchers first analyzed satellite radar imagery and environmental datasets using machine-learning techniques to identify areas most likely to contain buried archaeological remains. The resulting probability maps helped narrow the search area and guide detailed investigations on the ground.听
The team then applied Synthetic Aperture Radar (SAR) tomography, a technique that can provide information about subsurface structures by analyzing radar signals collected from multiple satellite passes. Areas identified as having archaeological potential were subsequently surveyed using Ground-Penetrating Radar (GPR), which sends electromagnetic pulses into the ground and records reflections from buried features.听
Dr. Diana Francis said: 鈥淭his study demonstrates how AI, satellite remote sensing and ground-based geophysical surveys can work together to reduce uncertainty in archaeological exploration. By combining large-scale satellite screening with targeted ground investigations, we can identify areas of interest more efficiently while minimizing disturbance to heritage sites.鈥澨�
Alisha Roy听
Science Writer听听
