Increasing availability and advancements of aerial Light Detection and Ranging (LiDAR) data have radically been shifting the way archeological surveys are performed. Unlike optical remote sensing imagery, LiDAR pulses travel through small gaps in dense tree canopies enabling archeologists to discover “hidden” past settlements and anthropogenic landscape features. While LiDAR has been increasingly adopted in archeological studies worldwide, its full potential is still being explored in the United States. Furthermore, while hand-digitizing features in remote-sensing datasets remain a valuable method for archeological surveys, it is often time- and labor intensive. The central objective of this research is to develop a geographic object-based image analysis-driven methodological framework linking low-level features and domain knowledge to automatically extract targets of interest from LiDAR-based digital terrain models (DTMs) and to closely examine the degree of interoperability of knowledge-based rulesets across different study sites focusing on the same semantic class. We apply this framework in southern New England, a geographic region in the northeastern United States where numerous seventeenth-century to early twentieth-century features such as relict charcoal hearths (RCHs), stone walls, and building foundations lie abandoned in densely forested terrain. Focusing on RCHs in this study, our results show promising agreement between manual and automated detection of these features. Overall, we show that the use of LiDAR data augmented with object-based classification workflows provides valuable baseline data for future archeological study and reconstruction of land-use/land-cover change over the past 300 years.

Advanced deep learning methods combined with regional, open access, airborne Light Detection and Ranging (LiDAR) data have great potential to study the spatial extent of historic land use features preserved under the forest canopy throughout New England, a region in the northeastern United States. Mapping anthropogenic features plays a key role in understanding historic land use dynamics during the 17th to early 20th centuries, however previous studies have primarily used manual or semi-automated digitization methods, which are time consuming for broad-scale mapping. This study applies fully-automated deep convolutional neural networks (i.e., U-Net) with LiDAR derivatives to identify relict charcoal hearths (RCHs), a type of historical land use feature. Results show that slope, hillshade, and Visualization for Archaeological Topography (VAT) rasters work well in six localized test regions (spatial scale: 15 degrees) (F1 score: 86.8%) compared to coniferous forest and low slope terrain (e.g., <15 degrees) (F1 score: 70.1%). Overall, our results contribute to current methodological discussions regarding automated extraction of historical cultural features using deep learning and LiDAR.