GEMAUT (2006–2026): A Brief History of a Robust and Open-Source Tool for the Automatic Generation of High-Resolution Digital Terrain Models from Satellite-Based Surface Models

Abstract. This contribution presents GEMAUT, a robust and open-source tool for the automatic generation of Digital Terrain Models (DTM) from high-resolution satellite-based Digital Surface Models (DSM). The paper provides a historical overview of the methods used for DTM extraction over the past twenty years, from early morphology-based filters to physically based optimization models and recent deep learning approaches. This retrospective is complemented by an analysis of the evolution of Earth-observation sensors, whose increasing spatial resolution now enables the application of LiDAR-oriented ground-filtering techniques directly to satellite DSM. The latest version of GEMAUT removes one of the main limitations of earlier implementations by eliminating the need for an external ground mask. Ground points are automatically extracted from the DSM using either the slope-based filter implemented in SAGA or the Cloth Simulation Filter available in PDAL. The terrain is then reconstructed through an energy-based surface optimization approach that combines robust data fidelity terms with curvature-based regularization. A second major contribution is the introduction of an automatic quality assessment module. By analysing DSM–DTM elevation differences, GEMAUT produces a spatialized precision mask that estimates the relative vertical accuracy at pixel level. This capability supports reliable quality control in operational and industrial workflows. The tool has been fully refactored, relies exclusively on open-source libraries, and is publicly released on GitHub to encourage transparency, reproducibility, and collaboration within the ISPRS community.