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.

The northeastern United States experienced extensive deforestation during the seventeenth through twentieth centuries primarily for European agriculture, which peaked in the mid-nineteenth century, and followed by widespread farmstead abandonment and reforestation. Analysis of airborne light detection and ranging (LiDAR) data has revealed thousands of historical land-use features with topographic signatures across the landscape under the region’s now-dense forest canopy. This study investigates two different types of features—stone walls and relict charcoal hearths—both of which are associated with widespread deforestation in the region. Our results demonstrate that LiDAR is an effective tool in reconstructing and quantifying the distribution and magnitude of historical forest cover using these relict land use features as a reliable proxy. Furthermore, these methods allow for direct quantification of cumulative land clearing over time in each town, in addition to the extent, intensity, and spatial distribution of cleared land and forest cover.