Accelerating the discovery of new Nasca geoglyphs using deep learning
Bibliographic Data
| ID | 11898388 |
|---|---|
| Authors | Masato Sakai (Yamagata University, corresponding author), Yiru Lai (IBM Research - Tokyo, corresponding author), Jorge Olano Canales (Université Paris 1 Panthéon-Sorbonne), Masao Hayashi (IBM Research - Tokyo), Kohhei Nomura (IBM Research - Tokyo) |
| Year | 2023 |
| Volume | 155 |
| Pages | 105777-105777 |
| Publication date | 2023-05-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Archaeological Science (JOURNAL) |
| Journal identifiers | ISSN: 0305-4403 • E-ISSN: 1095-9238 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.jas.2023.105777 |
| OpenAlex | W4378907710 |
| Language | EN |
| Citations received | 2 |
| References cited | 21 |
We discuss an archaeological research of employing deep learning (DL) based object detection on high-resolution aerial photographs to discover Nasca geoglyphs, which have been designated as a UNESCO World Heritage Site. Owing to extremely limited archaeological ground truth data and their differences in scale and design, it is difficult to detect new geoglyphs merely training DL on the known geoglyphs. Therefore, we developed a pipeline of DL to mine such data and address the challenges unique to archaeology. With this approach, we identified four new geoglyphs in the northern area of the Nasca Pampa, namely: a humanoid, a pair of legs, a fish, and a bird. The geoglyphs got verified through on-site surveys. We could identify new geoglyph's candidates approximately 21 times faster than with the naked eye alone. The approach would be beneficial for the future of archaeology in a new paradigm of combining field survey and AI
Archaeology · Deep learning · Field (mathematics · Geography · Ground truth · Object (grammar · Pipeline (software · World heritage · Archaeological Research and Protection · Archaeology and ancient environmental studies · Computer Science · Forensic Anthropology and Bioarchaeology Studies · Artificial Intelligence · Geology
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| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |