CC BY-NC-ND 4.0Schwencke, CédricCédricSchwenckeStütz, DominikDominikStützBulatov, DimitriDimitriBulatov2025-08-212025-08-212025https://publica.fraunhofer.de/handle/publica/490911https://doi.org/10.24406/publica-514910.5220/001316840000391210.24406/publica-5149Viewshed 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.enViewshed ComputationVisibility AnalysisNeural NetworksDigital Terrain ModelsDigital Surface ModelsSupervised LearningGISEnvironmental Monitoringconference paper