topoDL: A deep learning semantic segmentation dataset for the extraction of surface mine extents from historic USGS topographic maps

Input topographic maps, surface mine extents, and quad boundaries used in the following study:Maxwell, A.E., M.S. Bester, L.A. Guillen, C.A. Ramezan, D.J. Carpinello, Y. Fan, F.M. Hartley, S.M. Maynard, and J.L. Pyron, 2020. Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps, Remote Sensing, 12(24): 1-25. https://doi.org/10.3390/rs12244145.Associated code and descriptions of the data are provided on GitHub: https://github.com/maxwell-geospatial/topoDL. The surface mine extent data were obtained from the USGS prospect- and mine-related features from USGS topographic maps dataset: https://mrdata.usgs.gov/usmin/. Topographic maps were downloaded from TopoView/The National Map. We have simply prepared the data for easier ingestion into deep learning semantic segmentation workflows by aligning the vector polygon data with the associated topographic map and including topographic map boundaries to remove the collar information. Vector data can be rasterized and combined with the topographic maps to generate image chips and masks for semantic segmentation deep learning.The chip prep script on GitHub can be used to create chips and masks from these data. This compressed folder contains the following subfolders (ky_mines, ky_quads, ky_topos, oh_mines, oh_quads, oh_topos, va_mines, va_quads, va_topos). The mines folders contain the mine extents for each topographic map used in the study while the quads folders contain the quadrangle boundaries. All vector data are in shapefile format. The topos folders contain the topographic maps in TIFF format.

本数据集为下述研究的配套输入数据：包含地形图、露天矿区范围及图幅边界，相关研究为Maxwell, A.E.、M.S. Bester、L.A. Guillen、C.A. Ramezan、D.J. Carpinello、Y. Fan、F.M. Hartley、S.M. Maynard与J.L. Pyron于2020年发表于《遥感（Remote Sensing）》的《基于语义分割（semantic segmentation）深度学习（deep learning）从历史地形图中提取露天矿区范围》，刊载于2020年，第12卷第24期，页码1-25，DOI：10.3390/rs12244145。
相关代码与数据说明已发布于GitHub仓库：https://github.com/maxwell-geospatial/topoDL。
露天矿区范围数据源自美国地质调查局（United States Geological Survey, USGS）地形图图集中的勘探与矿区相关要素数据集，下载地址为：https://mrdata.usgs.gov/usmin/。地形图下载自TopoView/美国国家地图（The National Map）。
本数据集已完成预处理，以适配深度学习语义分割工作流：将矢量多边形（vector polygon）数据与对应地形图进行空间对齐，并添加地形图图幅边界以去除图廓边缘的冗余信息。矢量数据可被栅格化，并与地形图结合，生成用于语义分割深度学习的图像切片（image chips）与分割掩码（mask）。GitHub仓库中提供的切片预处理脚本可用于从本数据集中生成所需的图像切片与分割掩码。
本压缩包包含以下子文件夹：ky_mines、ky_quads、ky_topos、oh_mines、oh_quads、oh_topos、va_mines、va_quads、va_topos。其中，mines文件夹存储本研究中所用各地形图对应的矿区范围数据，quads文件夹存储对应图幅的边界数据，所有矢量数据均采用shapefile（形状文件）格式。topos文件夹存储TIFF（Tagged Image File Format，标签图像文件格式）格式的地形图文件。