Geospatial and Image Data 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

IF 1.1 0 ARCHAEOLOGY

Journal of Open Archaeology Data Pub Date : 2021-01-01 DOI:10.5334/joad.80

Weston L. A. Conner, Benjamin P. Carter, Jeff H. Blackadar

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引用次数: 1

Abstract

These data were used to build an object detection model to locate Relict Charcoal Hearths (RCH) as described in the paper “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” [1]. This is the first grouping of data for the paper above. The second grouping is also available in this journal, see “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”. These files consist of georeferenced Digital Elevation Model, Hillshade and Slope files derived from LiDAR for the State Game Lands (SGL). Included is a Shapefile and GeoJSON of State Game Land borders as well as the program used for downloading the LiDAR files. These data are stored on Zenodo.org. CORRESPONDING AUTHOR: Weston Conner archaeologist, author, data collection and verification, GIS expert, Lehigh University, US westonconner96@gmail.com

查看原文本刊更多论文

地理空间和图像数据来自“当计算机梦想木炭:使用深度学习，开放工具和开放数据来识别宾夕法尼亚州州立狩猎地及其周围的废弃木炭炉”论文

这些数据被用来建立一个物体检测模型来定位残余木炭炉(RCH)，如论文“当计算机梦想木炭:使用深度学习，开放工具和开放数据来识别宾夕法尼亚州州立禁猎区及其周围的残余木炭炉”[1]中所述。这是上述论文的第一组数据。第二组也可以在本刊中找到，参见“当计算机梦见木炭时:使用深度学习，开放工具和开放数据识别宾夕法尼亚州州立狩猎地及其周围的废弃木炭炉”论文中的“对象检测模型，图像数据和结果”。这些文件包括基于地理参考的数字高程模型、Hillshade和Slope文件，这些文件来自于国家禁猎区(SGL)的激光雷达。包括一个Shapefile和GeoJSON的国家游戏土地边界，以及用于下载激光雷达文件的程序。这些数据存储在Zenodo.org上。通讯作者:Weston Conner考古学家，作者，数据收集和验证，GIS专家，美国里海大学westonconner96@gmail.com

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Open Archaeology Data ARCHAEOLOGY-

CiteScore

2.20

自引率

14.30%

发文量

审稿时长

21 weeks