A major Mayan city, named Valeriana, has been uncovered beneath the dense jungles of Mexico, thanks to Lidar technology. The discovery was made by Luke Auld-Thomas, a PhD student at Tulane University, who found the Lidar dataset during an extensive Google search.
Valeriana was uncovered through a digital terrain model created from high-resolution airborne LiDAR data collected in 2013 using a RIEGL LMS-Q780 sensor. Originally gathered for a forest monitoring project led by the Nature Conservancy in Mexico, the dataset revealed over 6,500 structures spanning approximately 16.8 square kilometers. Researchers estimate that the city once housed up to 50,000 inhabitants.
The uncovered city exhibits hallmarks of a Classic Maya political capital, including a ball court, temple-style pyramids, enclosed plazas, and distinct urban blocks designated for agriculture, plaster production, and other specific purposes. An intricate cave system further underscores the city’s complexity.
Founded before 150 AD, Valeriana reached its peak between 750 and 850 AD. Its population density and urban planning surpass those of known ancient cities in Belize or Guatemala, offering new insights into the capabilities of the Maya civilization.
Insights from ancient urban life
The discovery illustrates the potential of Lidar technology in revolutionizing archaeological methods. Capable of mapping entire landscapes under dense forest cover, Lidar has enabled researchers to uncover extensive details about ancient urban life with minimal ground surveys.
The team plans to complement their analysis with fieldwork, aiming to further explore Valeriana’s significance. The city offers a unique opportunity to learn from ancient urban models, which could provide valuable lessons in addressing contemporary challenges of rapid urbanization.
Ancient cities, like Valeriana, showcase a wide range of urban living styles – from dense centers to sprawling agricultural hubs – suggesting that alternative approaches to modern city planning may draw inspiration from the past.
