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（ORBITaL-Net）是一项旨在促进建筑检测深度学习模型学习的训练数据集。该数据集由超过13万份独立样本组成，这些样本源自数千张单独的高分辨率卫星图像（平均分辨率为0.47米）。每个样本都是一个500x500像素的图像块，附带二元标签栅格，其中每个像素均由经过专业培训的图像分析员手工标注为建筑或非建筑。该数据集具有丰富的地理和语义多样性，包括来自北美洲、南美洲、非洲、中东和亚洲的样本，以及包含多种观察角度、地方建筑风格、土地利用/土地覆盖背景和大气条件的样本。