Convolutional neural networks for archaeological site detection – Finding “princely” tombs

Creating a quantitative overview over the early Iron Age heritage of the Eurasian steppes is a difficult task due to the vastness of the ecological zone and the often problematic access. Remote sensing based detection on open-source high-resolution satellite data in combination with convolutional neural networks (CNN) provide a potential solution to this problem. We create a CNN trained to detect early Iron Age burial mounds in freely available optical satellite data. The CNN provides a superior method for archaeological site detection based on the comparison to other detection algorithms trained on the same dataset. Throughout all comparison metrics (precision, recall, and score) the CNN performs best.