  Three-dimensional data have become increasingly present in earth observation
over the last decades. However, many 3D surveys are still underexploited due to
the lack of accessible and explainable automatic classification methods, for
example, new topo-bathymetric lidar data. In this work, we introduce
explainable machine learning for 3D data classification using Multiple
Attributes, Scales, and Clouds under 3DMASC, a new workflow. This workflow
introduces multi-cloud classification through dual-cloud features, encrypting
local spectral and geometrical ratios and differences. 3DMASC uses classical
multi-scale descriptors adapted to all types of 3D point clouds and new ones
based on their spatial variations. In this paper, we present the performances
of 3DMASC for multi-class classification of topo-bathymetric lidar data in
coastal and fluvial environments. We show how multivariate and embedded feature
selection allows the building of optimized predictor sets of reduced
complexity, and we identify features particularly relevant for coastal and
riverine scene descriptions. Our results show the importance of dual-cloud
features, lidar return-based attributes averaged over specific scales, and of
statistics of dimensionality-based and spectral features. Additionally, they
indicate that small to medium spherical neighbourhood diameters (<7 m) are
sufficient to build effective classifiers, namely when combined with
distance-to-ground or distance-to-water-surface features. Without using
optional RGB information, and with a maximum of 37 descriptors, we obtain
classification accuracies between 91 % for complex multi-class tasks and 98 %
for lower-level processing using models trained on less than 2000 samples per
class. Comparisons with classical point cloud classification methods show that
3DMASC features have a significantly improved descriptive power. Our
contributions are made available through a plugin in the CloudCompare software,
allowing non-specialist users to create classifiers for any type of 3D data
characterized by 1 or 2 point clouds (airborne or terrestrial lidar, structure
from motion), and two labelled topo-bathymetric lidar datasets, available on
https://opentopography.org/.
