  The increasing use of deep learning techniques has reduced interpretation
time and, ideally, reduced interpreter bias by automatically deriving
geological maps from digital outcrop models. However, accurate validation of
these automated mapping approaches is a significant challenge due to the
subjective nature of geological mapping and the difficulty in collecting
quantitative validation data. Additionally, many state-of-the-art deep learning
methods are limited to 2D image data, which is insufficient for 3D digital
outcrops, such as hyperclouds. To address these challenges, we present Tinto, a
multi-sensor benchmark digital outcrop dataset designed to facilitate the
development and validation of deep learning approaches for geological mapping,
especially for non-structured 3D data like point clouds. Tinto comprises two
complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain),
with spectral attributes and ground-truth data, and 2) a synthetic twin that
uses latent features in the original datasets to reconstruct realistic spectral
data (including sensor noise and processing artifacts) from the ground-truth.
The point cloud is dense and contains 3,242,964 labeled points. We used these
datasets to explore the abilities of different deep learning approaches for
automated geological mapping. By making Tinto publicly available, we hope to
foster the development and adaptation of new deep learning tools for 3D
applications in Earth sciences. The dataset can be accessed through this link:
https://doi.org/10.14278/rodare.2256.
