surficialDL: A geomorphology deep learning dataset of alluvium and thick glacial till derived form 1:24,000 scale surficial geology data for the western portion of Massachusetts, USA

surficialDL: A geomorpholgy deep learning dataset of alluvium and thick glacial till derived form 1:24,000 scale surficial geology data for the western portion of Massachusetts, USA scripts.zip arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background). makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool). surficialDL The digital terrain model associated with these data/project is available here: https://s3.us-east-1.amazonaws.com/download.massgis.digital.mass.gov/lidar/LIDAR_DEM_32BIT_FP.gdb.zip. alluvDL: polygons (vectors folder) and extents (extents folder) for alluvium features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology. tillDL: polygons (vector folder) and extents (extents folder) for thick till features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology.表层地质深度学习数据集（surficialDL）：源自美国马萨诸塞州西部1:24000比例尺表层地质数据的冲积层与厚冰碛物地貌学深度学习数据集。 scripts.zip与arcgisTools.atbx包含以下工具： 1. terrainDerivatives：从数字地形模型（Digital Terrain Model, DTM）生成地形衍生特征的工具，各波段含义为：波段1=50米半径圆形地形位置指数（TPI），波段2=坡度平方根，波段3=环形地形位置指数，波段4=山体阴影，波段5=多方向山体阴影，波段6=坡度阴影。 2. rasterizeFeatures：将矢量多边形转换为栅格掩膜的工具（要素区域值为1，背景区域值为0）。 配套的代码与工具如下： - makeChips.R：用于将地形衍生数据与目标样本裁剪为指定尺寸图像块的R函数。 - makeTerrainDerivatives.R：从数字地形数据生成6波段地形衍生特征的R函数，功能与ArcGIS Pro的同名工具一致。 - merge_logs.R：用于将多个训练日志合并为单个文件的R脚本。 - predictToExtents.ipynb：用于加载已训练模型并对新数据进行语义分割预测的Python笔记本。 - trainExperiments.ipynb：基于PyTorch与Segmentation Models库训练语义分割模型的Python笔记本。 - assessmentExperiments.ipynb：基于PyTorch与torchmetrics库生成模型评估指标的Python代码。 - graphs_results.R：使用ggplot2绘制可视化图表以汇总实验结果的R代码。 - makeChipsList.R：用于生成指定目录下图像块列表的R代码。 - makeMasks.R：从矢量数据生成栅格掩膜的R函数，功能与ArcGIS Pro的rasterizeFeatures工具一致。 本数据集配套的数字地形模型可通过以下链接获取：https://s3.us-east-1.amazonaws.com/download.massgis.digital.mass.gov/lidar/LIDAR_DEM_32BIT_FP.gdb.zip。 此外，本数据集包含两个子数据集： 1. alluvDL：包含冲积层要素的矢量多边形（存储于vector文件夹）与分区范围（存储于extents文件夹），并已划分为训练集、验证集与测试集。该数据集源自1:24000比例尺的马萨诸塞州表层地质数据集，官方下载地址为：https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology。 2. tillDL：包含厚冰碛物要素的矢量多边形（存储于vector文件夹）与分区范围（存储于extents文件夹），并已划分为训练集、验证集与测试集。该数据集源自1:24000比例尺的马萨诸塞州表层地质数据集，官方下载地址为：https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology。