Learning Digital Terrain Models From Point Clouds: ALS2DTM Dataset and Rasterization-Based GAN

Hoàng-Ân Lê, Florent Guiotte, Minh Tan Pham, Sébastien Lefèvre, Thomas Corpetti

Abstract:

Despite the popularity of deep neural networks
in various domains, the extraction of digital terrain models
(DTMs) from airborne laser scanning (ALS) point clouds is still
challenging. This might be due to the lack of dedicated large-scale
annotated dataset and the data-structure discrepancy between
point clouds and DTMs. To promote data-driven DTM extraction,
this paper collects from open sources a large-scale dataset
of ALS point clouds and corresponding DTMs with various
urban, forested, and mountainous scenes. A baseline method is
proposed as the first attempt to train a Deep neural network to
extract digital Terrain models directly from ALS point clouds via
Rasterization techniques, coined DeepTerRa. Extensive studies
with well-established methods are performed to benchmark the
dataset and analyze the challenges in learning to extract DTM
from point clouds. The experimental results show the interest of
the agnostic data-driven approach, with sub-metric error level
compared to methods designed for DTM extraction. The data and
source code is provided at https://lhoangan.github.io/deepterra/
for reproducibility and further similar research.