Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D images, 3868 flaked artifact and 2901 rock images used to train and test a machine learning model based on an openly available PyTorch implementation of Faster R-CNN ResNet 50.

Early tropical crop production in marginal subtropical and temperate Polynesia.

Polynesians introduced the tropical crop taro () to temperate New Zealand after 1280 CE, but evidence for its cultivation is limited. This contrasts with the abundant evidence for big game hunting, raising longstanding questions of the initial economic and ecological importance of crop production. Here we compare fossil data from wetland sedimentary deposits indicative of taro and leaf vegetable (including and spp.