mlsanityData to 14 Oct 2026

Learning Digital Terrain Models from Point Clouds: ALS2DTM Dataset and Rasterization-based GAN

arXiv 2206.03778 ↗First posted 8 Jun 2022cs.CV eess.IVcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 53 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

18 of 53 references.

References · 18

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6 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2025Shape Your Ground: Refining Road Surfaces Beyond Planar Representationscited by 2
  2. 2024Training point-based deep learning networks for forest segmentation with synthetic datacited by 1
  3. 2024Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasetscited by 1
  4. 2025GrounDiff: Diffusion-Based Ground Surface Generation from Digital Surface Modelscited by 0