Significance: Understanding agricultural subsistence is vital for understanding past complex societies. Lidar data are indicating widespread ancient Maya infrastructure. Wetland agriculture was crucial to ancient cultures, but no previous study coupled lidar with multiproxy evidence to demonstrate the extent and uses of Maya wetland fields. We conducted a lidar survey around wetlands that multiple use proxies established were ancient Maya polycultural systems. Lidar indicated the Birds of Paradise (BOP) wetland field complex was five times larger than we had previously mapped and identified an even larger wetland agroecosystem. We ground-verified the BOP fields through excavations and dating, creating a study to couple these multiproxy data with lidar, thereby demonstrating widespread ancient Maya wetland agroecosystems.

The objective of this project is to create a new implementation of a deep learning model that uses digital elevation data to detect shipwrecks automatically and rapidly over a large geographic area. This work is intended to apply a new methodology to the field of underwater archaeology. Shipwrecks represent a major resource to understand maritime human activity over millennia, but underwater archaeology is expensive, misappropriated, and hazardous. An automated tool to rapidly detect and map shipwrecks can therefore be used to create more accurate maps of natural and archaeological features to aid management objectives, study patterns across the landscape, and find new features. Additionally, more comprehensive and accurate shipwreck maps can help to prioritize site selection and plan excavation. The model is based on open source topo-bathymetric data and shipwreck data for the United States available from NOAA. The model uses transfer learning to compensate for a relatively small sample size and addresses a recurring problem that associated work has had with false positives by training the model both on shipwrecks and background topography. Results of statistical analyses conducted—ANOVAs and box and whisker plots—indicate that there are substantial differences between the morphologic characteristics that define shipwrecks vs. background topography, supporting this approach to addressing false positives. The model uses a YOLOv3 architecture and produced an F1 score of 0.92 and a precision score of 0.90, indicating that the approach taken herein to address false positives was successful.

Sky-earth, lake-sea: climate and water in Maya history and landscape - Volume 90 Issue 350 - Sheryl Luzzadder-Beach, Timothy Beach, Scott Hutson, Samantha Krause