ORBITaL-Net Training Library for Building Extraction

The Oak Ridge Building Image and TrAining Label Net (ORBITaL-Net), is a training dataset designed to enable the learning of building detection deep learning models. It consists of over 130,000 individual samples drawn from thousands of separate high resolution satellite images (average resolution 0.47 m). Each sample is a 500x500 pixel patch with accompanying binary label raster with each pixel hand-annotated by expertly trained image analysts as either building or non-building. This dataset has a large degree of geographic and semantic variety, including samples from North America, South America, Africa, the Middle East, and Asia, as well as samples that include a variety of viewing angles, vernacular architecture styles, LU/LC contexts, and atmospheric conditions.橡树岭建筑图像与训练标签网络（Oak Ridge Building Image and TrAining Label Net，ORBITaL-Net）是一款专为建筑检测深度学习模型训练打造的数据集。该数据集包含超过13万个独立样本，样本源自数千幅高分辨率卫星影像（平均分辨率0.47米）。每个样本为500×500像素的图像块，并附带二分类标签栅格，所有像素均由经过专业培训的图像分析师人工标注为建筑或非建筑。该数据集具备丰富的地理与语义多样性，样本覆盖北美、南美、非洲、中东及亚洲地区，同时包含多种观测角度、乡土建筑风格、土地利用/土地覆盖（LU/LC）场景以及不同大气条件下的样本。