Object Detection Model, Image Data and Results from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and Around State Game Lands in Pennsylvania” Paper

In the northeastern United States, widespread deforestation occurred during the 17–19th centuries as a result of Euro-American agricultural activity. In the late 19th and early 20th centuries, much of this agricultural landscape was reforested as the region experienced industrialization and farmland became abandoned. Many previous studies have addressed these landscape changes, but the primary method for estimating the amount and distribution of cleared and forested land during this time period has been using archival records. This study estimates areas of cleared and forested land using historical land use features extracted from airborne LiDAR data and compares these estimates to those from 19th century archival maps and agricultural census records for several towns in Massachusetts, a state in the northeastern United States. Results expand on previous studies in adjacent areas, and demonstrate that features representative of historical deforestation identified in LiDAR data can be reliably used as a proxy to estimate the spatial extents and area of cleared and forested land in Massachusetts and elsewhere in the northeastern United States. Results also demonstrate limitations to this methodology which can be mitigated through an understanding of the surficial geology of the region as well as sources of error in archival materials.

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Estimating Historically Cleared and Forested Land in Massachusetts, USA, Using Airborne LiDAR and Archival Records

Johnson

Ouimet

Dow

et al. 2021

Remote Sensing

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Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data

et al. 2021

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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: <1.5 km2, best F1 score: 95.5%), but also at broader extents at the town level (spatial scale: 493 km2, best F1 score: 86%). The model performed best in areas with deciduous forest and high slope terrain (e.g., >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.

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Object Detection Model, Image Data and Results from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and Around State Game Lands in Pennsylvania” Paper

Cited by 2 publications

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Estimating Historically Cleared and Forested Land in Massachusetts, USA, Using Airborne LiDAR and Archival Records

Estimating Historically Cleared and Forested Land in Massachusetts, USA, Using Airborne LiDAR and Archival Records

Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data

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