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

arXiv: https://arxiv.org/abs/2206.03778 · HTML: https://mlsanity.com/paper/2206.03778/

## 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 (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation](https://mlsanity.com/paper/2004.11985/) (2020)
- [RenderNet: A deep convolutional network for differentiable rendering from 3D shapes](https://mlsanity.com/paper/1806.06575/) (2018)
- [DublinCity: Annotated LiDAR Point Cloud and its Applications](https://mlsanity.com/paper/1909.03613/) (2019)
- [OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World](https://mlsanity.com/paper/2101.09641/) (2021)
- [Three for one and one for three: Flow, Segmentation, and Surface Normals](https://mlsanity.com/paper/1807.07473/) (2018)

## Built on by (most cited first, 4 of 4)

- [Shape Your Ground: Refining Road Surfaces Beyond Planar Representations](https://mlsanity.com/paper/2504.16103/) (2025)
- [Training point-based deep learning networks for forest segmentation with synthetic data](https://mlsanity.com/paper/2403.14115/) (2024)
- [Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasets](https://mlsanity.com/paper/2405.15394/) (2024)
- [GrounDiff: Diffusion-Based Ground Surface Generation from Digital Surface Models](https://mlsanity.com/paper/2511.10391/) (2025)

## 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.