  Mapping agencies are increasingly adopting Aerial Lidar Scanning (ALS) as a
new tool to monitor territory and support public policies. Processing ALS data
at scale requires efficient point classification methods that perform well over
highly diverse territories. To evaluate them, researchers need large annotated
Lidar datasets, however, current Lidar benchmark datasets have restricted scope
and often cover a single urban area. To bridge this data gap, we present the
FRench ALS Clouds from TArgeted Landscapes (FRACTAL) dataset: an
ultra-large-scale aerial Lidar dataset made of 100,000 dense point clouds with
high-quality labels for 7 semantic classes and spanning 250 km$^2$. FRACTAL is
built upon France's nationwide open Lidar data. It achieves spatial and
semantic diversity via a sampling scheme that explicitly concentrates rare
classes and challenging landscapes from five French regions. It should support
the development of 3D deep learning approaches for large-scale land monitoring.
We describe the nature of the source data, the sampling workflow, the content
of the resulting dataset, and provide an initial evaluation of segmentation
performance using a performant 3D neural architecture.
