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In the summer, Dr. Gino Caspari\u2019s day starts at 5:30 a.m. in Siberia, where he studies the ancient Scythians with the Swiss National Science Foundation. There, he looks for burial places of these nomadic warriors who rode through Asia 2,500 years ago. The work isn\u2019t easy, from dealing with extreme temperatures, to swamps covered with mosquitos. But the biggest challenge is staying one step ahead of tomb raiders.<\/p>\n
It\u2019s believed that more than 90% of the tombs \u2014 called kurgans \u2014 have already been destroyed by raiders looking to profit off what they find, but Gino is looking for the thousands he believes remain scattered across Russia, Mongolia and Western China. To track his progress, he began mapping these burial sites using Google Earth. \u201cThere\u2019s a plethora of open data sources out there, but most of them don\u2019t have the resolution necessary to detect individual archaeological structures,\u201d Dr. Caspari says, pointing out that getting quality data is also very expensive. \u201cGoogle Earth updates high-res data across the globe, and, especially in remote regions, it was a windfall for archaeologists. Google Earth expanded our possibilities to plan surveys and understand cultural heritage on a broader geographic scale.\u201d<\/p>\n
While Google Earth helped Dr. Caspari plan his expeditions, he still couldn\u2019t stay ahead of the looters. He needed to get there faster. That\u2019s when he met data scientist Pablo Crespo and started using another Google tool, TensorFlow.<\/p>\n<\/div>\n<\/div>\n
\u201cSince I started my PhD in 2013, I have been interested in automatic detection of archaeological sites from remote sensing data,\u201d Gino says. \u201cIt was clear we needed to look at landscapes and human environmental interaction to understand past cultures. The problem was that our view was obscured by a lack of data and a focus on individual sites.\u201d Back then, he tried some simple automatization processes to detect the places he needed for his research with the available technology, but only got limited results. In 2020, though, Gino and Pablo created a machine learning model using TensorFlow that could analyze satellite images they pulled from Google Earth. This model would look for places on the images that had the characteristics of a Scythian tomb.<\/p>\n<\/div>\n<\/div>\n
The progress in the field of machine learning has been insanely fast, improving the quality of classification and detection to a point where it has become much more than just a theoretical possibility. Google\u2019s freely available technologies have help<\/q><\/div>\n<\/div>\n<\/section>\n<\/div>\n<\/div>\n\n\nThis technology sped up the discovery process for Gino, giving him an advantage over looters and even deterioration caused by climate change.<\/p>\n
\u201cFrankly, I think that without these tools, I probably wouldn\u2019t have gotten this far in my understanding of technology and what it can do to make a difference in the study of our shared human past,\u201d Gino says. \u201cAs a young scholar, I just lack the funds to access a lot of the resources I need. Working with Pablo and others has widened my perspective on what is possible and where we can go.\u201d<\/p>\n
Technology solutions have given Dr. Caspari\u2019s work a new set of capabilities, supercharging what he\u2019s able to do. And it\u2019s also made him appreciate the importance of the human touch. \u201cThe deeper we dive into our past with the help of technology, the more apparent it becomes how patchy and incomplete our knowledge really is,\u201d he says. \u201cTechnology often serves as an extension of our senses and mitigates our reality. Weaving the fabric of our reality will remain the task of the storyteller in us.\u201d<\/p>\n<\/div>\n<\/div>\n<\/div>\n
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