5m Resolution Digital Terrain Model for Italy
Abstract
This dataset provides a seamless high-resolution Digital Terrain Model of the Italian territory at 5 m spatial resolution (HR-DTM-5m). The model was generated by integrating heterogeneous airborne LiDAR-derived DTMs, available at 1–2 m resolution over large but spatially fragmented portions of Italy, with the national TINITALY 1.1 digital terrain model at 10 m resolution, which was used as the sole elevation source in areas not covered by LiDAR data. The dataset was produced through a structured and fully reproducible workflow including data harmonisation, regional mosaicking of homogeneous LiDAR datasets, resampling to a common grid, and systematic blending of adjacent and partially overlapping elevation sources. Prior to blending, systematic vertical offsets between LiDAR-derived DTMs and TINITALY were corrected to ensure a consistent vertical reference. Smooth transition zones were then applied to minimise elevation discontinuities and ensure spatial continuity across dataset boundaries. The HR-DTM-5m prioritises morphological realism and topographic continuity over the strict optimisation of point-wise elevation accuracy. This design choice reflects the intended use of the dataset for geomorphological and hydrological applications, including flood modelling, landslide susceptibility analysis, and the study of rapid surface processes at regional to national scale. The dataset provides complete national coverage on a single 5 m grid, preserving the high level of detail of LiDAR data where available, while relying on the resampled TINITALY model in areas without LiDAR coverage. It is conceived as an operational and extensible product, allowing the integration of new LiDAR acquisitions without recomputing the entire national model.
Fields of Study & Subjects
Remote Sensing and LiDAR Applications
Satellite Image Processing and Photogrammetry
3D Surveying and Cultural Heritage
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 26 February 2026
- Language
- EN
- Document type
- Dataset