SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information

The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular). The dataset consists of 777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation. The structure of the data is as follows:<br> 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks.<br> 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials.<br> Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type. 1-Road-Ortho |----Train |----Test -----Validation 1-Road-Mask |----Train |----Test -----Validation 2-NoRoad-Ortho |----Train |----Test -----Validation本数据集采用Python开发的脚本生成，所用开源库包括GDAL/OGR与MapScript，用于对代表不同类型道路（城市道路、城际道路与乡村道路）轴线的矢量制图进行栅格化处理。该矢量制图数据源自西班牙多个官方渠道（国家地理研究院以及各自治区制图机构），我们已以细致且系统的方式对其进行修订与编辑，以确保地图上的道路与2021年1月1日西班牙国家地理信息中心（CNIG）下载中心提供的正射影像（orthoimages）一致。数据集覆盖西班牙本土与岛屿的16块矩形区域（尺寸为28.5 km × 18.5 km），共包含777599张256×256像素的PNG格式图像，按训练、测试与验证任务划分至不同文件夹中。 数据集结构如下： 1. 1-Road-Ortho与1-Road-Mask文件夹分别存储用于训练语义分割网络的图像与真值标签（ground truth）； 2. 1-Road-Ortho与2-NoRoad-Ortho文件夹存储包含或不包含vials的航空影像，用于训练二元镶嵌分割网络以识别含vials的镶嵌区块。 此外，每个文件夹的内部结构统一：训练集、测试集与验证集分别占总图像与各类型掩码的90%、5%与5%。具体目录结构如下： 1-Road-Ortho |----Train |----Test -----Validation 1-Road-Mask |----Train |----Test -----Validation 2-NoRoad-Ortho |----Train |----Test -----Validation