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. (27th December 2021) - Blackadar, Jeff; Carter, Benjamin; Conner, Weston
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 second grouping of data for the paper above. The first grouping is also available in this journal, see "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" [2 ]. These files consist of: JPEGs representing tiles of larger Slope TIFF files derived from LiDAR for the State Game Lands (SGL) of Pennsylvania, United States [3 4 5 6 ]. A subset of these tiles was used to train the model. A Shapefile of points of known relict charcoal hearths (RCH). XML files representing the pixel points of known RCHs on JPEG files used for training. Jupyter notebooks of programs used to prepare data and train a Mask R-CNN model. The Mask R-CNN model H5 file. Shapefile and GeoJSON of object detection results from the model showing locations of possible RCH in all SGLs. XML files representing the pixel points of predicted RCH on JPEG files used for predictions. GeoJSON of results using cluster analysis. These data are stored onzenodo.org . The programs are stored on Github.com .