Inria Aerial Image Labeling Dataset

The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery. Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0.3 m Ground truth data for two semantic classes: building and not building (publicly disclosed only for the training subset) The images cover dissimilar urban settlements, ranging from densely populated areas (e.g., San Francisco’s financial district) to alpine towns (e.g,. Lienz in Austrian Tyrol). Instead of splitting adjacent portions of the same images into the training and test subsets, different cities are included in each of the subsets. For example, images over Chicago are included in the training set (and not on the test set) and images over San Francisco are included on the test set (and not on the training set). The ultimate goal of this dataset is to assess the generalization power of the techniqu

Inria航空图像标注数据集针对遥感领域的一项核心议题——对航空图像进行自动像素级标注。数据集特点：覆盖面积达810平方公里（其中405平方公里用于训练，405平方公里用于测试）；采用空间分辨率为0.3米的航空正射彩色影像；提供两种语义类别（建筑物与非建筑物）的地面真实数据（仅训练子集公开）；图像覆盖了多样化的城市聚落，从人口密集区（例如，旧金山的金融区）到山地城镇（例如，奥地利蒂罗尔的莱恩茨）；为避免将同一图像的相邻部分分割到训练集和测试集中，每个子集中包含不同的城市。例如，芝加哥上空的图像包含在训练集中（而不在测试集中），而旧金山上空的图像包含在测试集中（而不在训练集中）。该数据集的最终目标是评估技术的泛化能力。