Over 300 New 'Nazca Lines' Geoglyphs Have Been Revealed by AI
Advanced AI has led to the discovery of over 300 new geoglyphs in the Nazca Pampa, nearly doubling the known total and revealing insights into ancient cultural practices and purposes.
Read original articleAdvanced artificial intelligence (AI) has facilitated the discovery of over 300 new geoglyphs in the Nazca Pampa, nearly doubling the previously known total of 430. This research, led by Masato Sakai from Yamagata University, utilized AI imaging technology to identify these ancient artworks, some dating back 2,000 years. The geoglyphs, located in a desert area of Peru, have survived due to the region's environmental conditions. The study categorizes the geoglyphs into five potential purposes: calendar and astronomy, geometry, agriculture and irrigation, movement or communication, and artistic expression. The newly identified geoglyphs include various styles, with a significant number depicting humans and domestic animals. The research suggests that these relief-type geoglyphs were likely created for visibility from nearby trails, possibly serving cultural or ceremonial functions. The findings highlight the potential for further discoveries in the Nazca region, emphasizing the role of AI in archaeological research. The study was published in the Proceedings of the National Academy of Sciences, marking a significant advancement in understanding the cultural practices of the ancient Nazca civilization.
- Over 300 new Nazca Lines geoglyphs have been discovered using AI technology.
- The total number of known geoglyphs has nearly doubled, revealing insights into ancient cultures.
- The geoglyphs are categorized into five potential purposes, including artistic expression and communication.
- Many newly identified geoglyphs depict humans and domestic animals, suggesting cultural significance.
- The study underscores the transformative impact of AI in archaeological research and discovery.
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1. Collected data by flying aircraft over the area. Used a land classification mask to restrict the are to ~ 600 sq km
2. Make image patches of 11m by 11m. I believe there is some overlap in the patches. Sharpen the images for contrast.
3. The training data comes from previously known glyphs. Positive label patches are ones with a glyph. Negative label patches are randomly sampled from the vicinity of the glyph.
4. It looks like they fine tuned resnet 50 with these labels
5. Ran inference on other patches. They had false positives
6. Manually verified these AI predicted glyphs by ground surveys
I couldn't figure out how they drew the outlines in the pictures. I guess it was manually done
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