physioDL: A dataset for geomorphic deep learning representing a scene classification task (predict physiographic region in which a hilshade occurs)

<b>physioDL: </b>A dataset for geomorphic deep learning representing a scene classification task (predict physiographic region in which a hilshade occurs)<b>Purpose: </b>Datasets for geomorphic deep learning. Predict the physiographic region of an area based on a hillshade image. Terrain data were derived from the 30 m (1 arc-second) 3DEP product across the entirety of CONUS. Each chip has a spatial resolution of 30 m and 256 rows and columns of pixels. As a result, each chip measures 7,680 meters-by-7,680 meters. Two datasets are provided. Chips in the hs folder represent a multidirectional hillshade while chips in the ths folder represent a tinted multidirectional hillshade. Data are represented in 8-bit (0 to 255 scale, integer values). Data are projected to the Web Mercator projection relative to the WGS84 datum. Data were split into training, test, and validation partitions using stratified random sampling by region. 70% of the samples per region were selected for training, 15% for testing, and 15% for validation. There are a total of 16,325 chips. The following 22 physiographic regions are represented: "ADIRONDACK" , "APPALACHIAN PLATEAUS", "BASIN AND RANGE", "BLUE RIDGE", "CASCADE-SIERRA MOUNTAINS", "CENTRAL LOWLAND", "COASTAL PLAIN", "COLORADO PLATEAUS", "COLUMBIA PLATEAU", "GREAT PLAINS", "INTERIOR LOW PLATEAUS", "MIDDLE ROCKY MOUNTAINS", "NEW ENGLAND", "NORTHERN ROCKY MOUNTAINS", "OUACHITA", "OZARK PLATEAUS", "PACIFIC BORDER", and "PIEDMONT", "SOUTHERN ROCKY MOUNTAINS", "SUPERIOR UPLAND", "VALLEY AND RIDGE", "WYOMING BASIN". Input digital terrain models and hillshades are not provided due to the large file size (&gt; 100GB). <b>Files</b>physioDL.csv: Table listing all image chips and associated physiographic region (id = unique ID for each chip; region = physiographic region; fnameHS = file name of associated chip in hs folder; fnameTHS = file name of associated chip in ths folder; set = data split (train, test, or validation).chipCounts.csv: Number of chips in each data partition per physiographic province. map.png: Map of data.makeChips.R: R script used to process the data into image chips and create CSV files.<b>inputVectors</b>chipBounds.shp = square extent of each chipchipCenters.shp = center coordinate of each chipprovinces.shp = physiographic provincesprovinces10km.shp = physiographic provinces with a 10 km negative buffer

**physioDL：** 面向地貌深度学习的数据集，用于场景分类任务（预测晕渲图（hillshade）所属的地貌区域）。

**用途：** 本数据集服务于地貌深度学习研究，旨在基于晕渲图像预测区域所属的地貌区域。地形数据源自美国本土全域（CONUS）的30米（1弧秒）3DEP产品。每个图像块（chip）的空间分辨率为30米，像素维度为256行×256列，因此单块图像的覆盖范围为7680米×7680米。本数据集包含两个子数据集：hs文件夹内的图像块为多向晕渲图，ths文件夹内的图像块为着色多向晕渲图。数据采用8位整型格式存储，取值范围为0至255，投影坐标系为基于WGS84椭球体的Web Mercator投影。数据集按照地貌区域进行分层随机抽样，划分为训练集、测试集与验证集：每个区域的样本中70%用于训练，15%用于测试，15%用于验证。数据集总计包含16325个图像块，涵盖以下22个地貌区域："ADIRONDACK"、"APPALACHIAN PLATEAUS"、"BASIN AND RANGE"、"BLUE RIDGE"、"CASCADE-SIERRA MOUNTAINS"、"CENTRAL LOWLAND"、"COASTAL PLAIN"、"COLORADO PLATEAUS"、"COLUMBIA PLATEAU"、"GREAT PLAINS"、"INTERIOR LOW PLATEAUS"、"MIDDLE ROCKY MOUNTAINS"、"NEW ENGLAND"、"NORTHERN ROCKY MOUNTAINS"、"OUACHITA"、"OZARK PLATEAUS"、"PACIFIC BORDER"、"PIEDMONT"、"SOUTHERN ROCKY MOUNTAINS"、"SUPERIOR UPLAND"、"VALLEY AND RIDGE"、"WYOMING BASIN"。由于原始数字地形模型与晕渲图文件体积超过100GB，故未提供输入数据。

**文件说明：**
physioDL.csv：收录所有图像块及其对应地貌区域的表格文件，字段包括：id（每个图像块的唯一标识符）、region（所属地貌区域）、fnameHS（hs文件夹中对应图像块的文件名）、fnameTHS（ths文件夹中对应图像块的文件名）、set（数据划分类型，含train、test或validation）。
chipCounts.csv：按地貌区域统计各数据划分下的图像块数量。
map.png：数据集分布地图。
makeChips.R：用于将地形数据处理为图像块并生成CSV文件的R语言脚本。

**输入矢量数据：**
chipBounds.shp：各图像块的方形范围矢量文件
chipCenters.shp：各图像块的中心点坐标矢量文件
provinces.shp：地貌区域矢量文件
provinces10km.shp：带有10公里负缓冲区的地貌区域矢量文件