AI-Accelerated Viewshed Computation for High-Resolution Elevation Models

Cédric Schwencke, Dominik Stütz, Dimitri Bulatov

2025

Abstract

Viewshed computation, essential for visibility analysis in GIS applications, involves determining visible areas from a given point using the digital terrain model (DTM) and digital surface model (DSM). The traditional methods, though accurate, can be computationally intensive, especially with increasing search distances and high-resolution elevation DSMs. This paper introduces a novel approach leveraging neural networks to estimate the farthest visible point (FVP). At this point the viewshed computation could be aborted, which significantly reducing computation time without compromising accuracy. The proposed method employs a fully connected neural network trained on varied terrain profiles, achieving over 99% accuracy in visibility predictions while reducing the required amount of computations by more than 90%. This approach demonstrates substantial performance gains, making it suitable for applications requiring fast visibility analysis.

Paper Citation

in Harvard Style

Schwencke C., Stütz D. and Bulatov D. (2025). AI-Accelerated Viewshed Computation for High-Resolution Elevation Models. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP; ISBN 978-989-758-728-3, SciTePress, pages 251-258. DOI: 10.5220/0013168400003912

in Bibtex Style

@conference{grapp25,
author={Cédric Schwencke and Dominik Stütz and Dimitri Bulatov},
title={AI-Accelerated Viewshed Computation for High-Resolution Elevation Models},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP},
year={2025},
pages={251-258},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013168400003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP
TI - AI-Accelerated Viewshed Computation for High-Resolution Elevation Models
SN - 978-989-758-728-3
AU - Schwencke C.
AU - Stütz D.
AU - Bulatov D.
PY - 2025
SP - 251
EP - 258
DO - 10.5220/0013168400003912
PB - SciTePress