Miola, MariannaCabiddu, DanielaPittaluga, SimoneRaviola, MicaelaZuccolini, Marino VetuschiComino Trinidad, MarcMancinelli, ClaudioMaggioli, FilippoRomanengo, ChiaraCabiddu, DanielaGiorgi, Daniela2025-11-212025-11-212025978-3-03868-296-72617-4855https://doi.org/10.2312/stag.20251318https://diglib.eg.org/handle/10.2312/stag20251318Understanding and modeling the complex geometries of the natural world is a key challenge in the Geosciences. Representing terrains, subsurfaces, catchments, and environmental domains requires not only accurate data acquisition but also robust and flexible geometric modeling tools. While computer graphics and geometry processing have made significant advances in representing and manipulating complex 2D and 3D shapes, their uptake in environmental modeling remains limited due to the lack of geoscientific constraints and interoperability with geospatial standards. We present MUSE-geometry, a lightweight computational geometry tool designed to bridge this gap. Our tool integrates geometric primitives, structured and unstructured meshing, and editing operations with explicit support for geospatial data formats and coordinate reference systems. By combining computational geometry with geospatial awareness, the tool enables the creation of simulation-ready 2D/3D models that preserve input integrity and topological consistency. Implemented as a selfcontained command-line application in C++, our tool has been tested on real-world scenarios, including modeling topographic surfaces or coastal water volumes, demonstrating its ability to unify data structures and geometric processing within a single pipeline. The tool offers flexible and interoperable operations that enhance the integration of advanced geometric processing into geoscientific workflows in compliance with geospatial standards.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Mesh geometry models; Applied computing → Environmental sciencesComputing methodologies → Mesh geometry modelsApplied computing → Environmental sciences10.2312/stag.2025131813 pages