Using Simulated Training Data to Locate Archaeological Sites with Machine Learning

Abstract Archaeologists have demonstrated the value of deep learning models for detecting archaeological objects in lidar data. As landscape-level projects become the norm, archaeological data derived from deep learning predictions can be integrated into these initiatives through coupled natural-cultural landscapes planning. However, the paucity of archaeological training datasets limits the application of deep learning models to relatively common and well-documented object classes. Using procedurally generated training datasets may be one approach to overcome this bottleneck. To test the efficacy of procedural generation for developing deep learning training data, we trained models to detect a novel object class (hypothesized historic tar kilns) in the Kisatchie National Forest in Louisiana. We developed two procedural generation approaches to embed simulated archaeological objects in a lidar-derived DEM and used these datasets to train deep learning (Mask R-CNN) models. We then evaluated model predictions within lidar-derived visualizations and during field survey. Our trained models detected targets with high recall but low precision. Field investigation suggested that the objects were not tar kilns but a different historic feature class. This study suggests that models trained on simulated objects are a useful addition to lidar analysis tool kits and can be directly integrated into archaeological field investigation workflows.