Abstract<\/strong><\/p>\n\n\n\n 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 \u201cfields\u201d 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.<\/p>\n\n\n\n ______________________________________________________________________________<\/p>\n\n\n\n Graham, S., and Simons, J., 2021, Listening to Dura Europos: An Experiment in Archaeological Image Sonification<\/a>. Internet Archaeology<\/em> 56. <\/p>\n\n\n\n Excerpt from the Introduction<\/strong><\/p>\n\n\n\n Jennifer Baird\nwrites, of the Yale University\u2013French Academy Expedition to Dura Europos in\n1928-1937 and the way the expedition composed photographs for record keeping\nand publication, ‘What might an archaeology of Dura have looked like if objects\nhad been photographed as assemblages from single contexts rather than in\ntypological or material-based groups?’ (2011<\/a>, 442).<\/p>\n\n\n\n We ask instead, what\nmight the archaeology of Dura Europos sound<\/em> like?<\/p>\n\n\n\n What can we\u00a0hear<\/em>, if we extend our senses through digital prostheses to extend our ability to sense the world? In this article we present an experiment in sonifying the archaeological imagery analysed by Baird (2011<\/a>) as a way of enhancing our engagement with these archival photographs.<\/p>\n\n\n\n ______________________________________________________________________________<\/strong><\/p>\n\n\n\n Morrison, K., E. Hammer, O. Boles, M. Madella, N. Whitehouse, M.-J. Gaillard, J. Bates, M. Vander Linden, S. Merlo, A. Yao, L. Popova, A.C., F. Antolin, A. Bauer, S. Biagetti, R. Bishop, P. Buckland, P. Cruz, D. Dreslerova, G. Dusseldorp, E. Ellis, D. Filipovic, T. Foster, M. Hannaford, S. Harrison, M. Hazarika, H. Herold, J. Hilpert, J. Kaplan, A. Kay, K.K. Goldewijk, J. Kolar, E. Kyazike, J. Laabs, C. Lancelotti, P. Lane, D. Lawrence, K. Lewis, U. Lombardo, G. Lucarini, M. Arroyo-Kalin, R. Marchant, M. McClatchie, M. McLeester, S. Mooney, M. Moskal-del Hoyo, V. Navarrete, E. Ndiema, M. Nowak, W. Out, C. Petrie, L. Phelps, Z. Pinke, T. Russell, A. Sluyter, A. Styring, S. Veerasamy, L. Welton<\/strong>, and M. Zanon. (2021) \u201cMapping past human land use using archaeological data: a new classification for global land use synthesis and data harmonization\u201d<\/a>. PLoS ONE<\/em> 16(4): e0246662. <\/p>\n\n\n\n Abstract<\/strong><\/p>\n\n\n\n In the 12,000 years preceding the\nIndustrial Revolution, human activities led to significant changes in land\ncover, plant and animal distributions, surface hydrology, and biochemical\ncycles. Earth system models suggest that this anthropogenic land cover change\ninfluenced regional and global climate. However, the representation of past\nland use in earth system models is currently oversimplified. As a result, there\nare large uncertainties in the current understanding of the past and current\nstate of the earth system. In order to improve representation of the variety\nand scale of impacts that past land use had on the earth system, a global\neffort is underway to aggregate and synthesize archaeological and historical\nevidence of land use systems. Here we present a simple, hierarchical\nclassification of land use systems designed to be used with archaeological and\nhistorical data at a global scale and a schema of codes that identify land use\npractices common to a range of systems, both implemented in a geospatial database.\nThe classification scheme and database resulted from an extensive process of\nconsultation with researchers worldwide. Our scheme is designed to deliver\nconsistent, empirically robust data for the improvement of land use models,\nwhile simultaneously allowing for a comparative, detailed mapping of land use\nrelevant to the needs of historical scholars. To illustrate the benefits of the\nclassification scheme and methods for mapping historical land use, we apply it\nto Mesopotamia and Arabia at 6 kya (c. 4000 BCE). The scheme will be used to\ndescribe land use by the Past Global Changes (PAGES) LandCover6k working group,\nan international project comprised of archaeologists, historians, geographers,\npaleoecologists, and modelers. Beyond this, the scheme has a wide utility for\ncreating a common language between research and policy communities, linking\narchaeologists with climate modelers, biodiversity conservation workers and\ninitiatives.<\/p>\n\n\n\n ______________________________________________________________________________<\/p>\n\n\n\n![\"\"](\"https:\/\/insidecrane.utoronto.ca\/wp-content\/uploads\/2021\/11\/urn_cambridge.org_id_binary_20210828130754241-0516_S2326376821000176_S2326376821000176_fig4.png\")