  We introduce ECLAIR (Extended Classification of Lidar for AI Recognition), a
new outdoor large-scale aerial LiDAR dataset designed specifically for
advancing research in point cloud semantic segmentation. As the most extensive
and diverse collection of its kind to date, the dataset covers a total area of
10$km^2$ with close to 600 million points and features eleven distinct object
categories. To guarantee the dataset's quality and utility, we have thoroughly
curated the point labels through an internal team of experts, ensuring accuracy
and consistency in semantic labeling. The dataset is engineered to move forward
the fields of 3D urban modeling, scene understanding, and utility
infrastructure management by presenting new challenges and potential
applications. As a benchmark, we report qualitative and quantitative analysis
of a voxel-based point cloud segmentation approach based on the Minkowski
Engine.
