# Void Filling of Digital Elevation Models with Deep Generative Models

arXiv: https://arxiv.org/abs/1811.12693 · HTML: https://mlsanity.com/paper/1811.12693/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [A systematic review and meta-analysis of Digital Elevation Model (DEM) fusion: pre-processing, methods and applications](https://mlsanity.com/paper/2203.15026/) (2022)
- [RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion](https://mlsanity.com/paper/2404.09290/) (2024)
- [Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling](https://mlsanity.com/paper/2501.15440/) (2025)
- [Digital Elevation Model Estimation from RGB Satellite Imagery using Generative Deep Learning](https://mlsanity.com/paper/2511.21985/) (2025)
- [Inpainting the Red Planet: Diffusion Models for the Reconstruction of Martian Environments in Virtual Reality](https://mlsanity.com/paper/2510.14765/) (2025)
- [Test-Time Adaptation for Height Completion via Self-Supervised ViT Features and Monocular Foundation Models](https://mlsanity.com/paper/2604.02009/) (2026)
- [Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning](https://mlsanity.com/paper/2609.31199/) (2026)

## Abstract (arXiv)

In recent years, advances in machine learning algorithms, cheap computational resources, and the availability of big data have spurred the deep learning revolution in various application domains. In particular, supervised learning techniques in image analysis have led to superhuman performance in various tasks, such as classification, localization, and segmentation, while unsupervised learning techniques based on increasingly advanced generative models have been applied to generate high-resolution synthetic images indistinguishable from real images. In this paper we consider a state-of-the-art machine learning model for image inpainting, namely a Wasserstein Generative Adversarial Network based on a fully convolutional architecture with a contextual attention mechanism. We show that this model can successfully be transferred to the setting of digital elevation models (DEMs) for the purpose of generating semantically plausible data for filling voids. Training, testing and experimentation is done on GeoTIFF data from various regions in Norway, made openly available by the Norwegian Mapping Authority.