History alumnus current MA in History with Data Science student Jeff H. Blackadar and colleagues Benjamin P. Carter and Weston L. A. Conner have just published a new article in the Advances in Archaeological Practice journal. The full article, “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” is available for viewing online. The abstract and bibliographic details can be found below.
This research employs machine learning (Mask Region-Based Convolutional Neural Networks [Mask R-CNN]) and cluster analysis (Density-based spatial clustering of applications with noise [DBSCAN]) to identify more than 20,000 relict charcoal hearths (RCHs) organized in large “fields” within and around State Game Lands (SGLs) in Pennsylvania. This research has two important threads that we hope will advance the archaeological study of landscapes. The first is the significant historical impact of charcoal production, a poorly understood industry of the late eighteenth to early twentieth century, on the historic and present landscape of the United States. Although this research focuses on charcoal production in Pennsylvania, it has broad application for both identifying and contextualizing historical charcoal production throughout the world and for better understanding modern charcoal production. The second thread is the use of open data, open source, and open access tools to conduct this analysis, as well as the open publication of the resultant data. Not only does this research demonstrate the significance of open access tools and data but the open publication of our code as well as our data allow others to replicate our work, to tweak our code and protocols for their own work, and reuse our results.
Carter, B., Blackadar, J., & Conner, W. (2021). 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. Advances in Archaeological Practice, 1-15. doi:10.1017/aap.2021.17