Terrain Modelling and Visualization

After almost 2 years of testing and fine-tuning, we finally have an Ensemble Digital Terrain Model of the world and some 15+ standard DTM parameters / land surface variables at 30 m. Download from: https://lnkd.in/eVW52Rig as #OpenData Great work by Yu-Feng Ho with contributions by John Lindsay, Hannes Isaak Reuter and others / fellow #Geomorphometry researchers. We used over 30 billion training points (ICESat-2 and GEDI) to fit locally optimized models per tile and produce canopy-free terrain (bare-earth) model from Copernicus DEM, ALOS World3D, and object height models. GEDTM will be continuously updated as a part of the #OpenEarthMonitor project funded by #Horizon_Europe EU Science, Research and Innovation . Our little contribution to the OpenTopography for everyone. Access the preprint of the paper here: https://lnkd.in/edBkuU8X. If you spot an issue or bug please report via Github issues. Next mission: produce ensemble of national and global terrain models! 

🌍 Generating a High-Resolution 10m DEM Using Sentinel-1 SAR Data 🛰️ Digital Elevation Models (DEMs) are essential for understanding terrain, modeling water flow, and assessing flood risks. In this project, I used Sentinel-1 Synthetic Aperture Radar (SAR) data to create a high-resolution 10-meter DEM, showcasing the power of remote sensing for geospatial analysis. 🔍 Workflow Overview: 🗂️ Data Acquisition: Downloaded Sentinel-1 SLC images from the Copernicus Open Access Hub and verified perpendicular and temporal baselines using the Alaska Satellite Facility (ASF). 🛠️ DEM Generation: Processed the data in SNAP, including coregistration, interferogram formation, phase filtering, and phase unwrapping to ensure high accuracy and detailed elevation modeling. 🗺️ Final DEM Validation: Used ArcGIS for visual inspection, clipping to the study area, and creating final elevation maps with hydrological features like water flow networks. This project highlights the incredible speed, reliability, and precision of Sentinel-1 SAR data for DEM generation. I’m excited to share the detailed step-by-step guide I created — from data collection to final map production — to help others navigate this process and generate their own high-resolution DEMs. #RemoteSensing #GIS #Sentinel1 #DEM #Geospatial #EarthObservation #ArcGIS #SNAP #SARData #SpatialAnalysis #Mapping

GEDTM30 – A Global 1-Arc-Second (~30m) Digital Terrain Model (DTM) -- https://lnkd.in/gPaYb93T <-- shared GitHub repository -- H/T Hamish Campbell “GEDTM30 is a global 1-arc-second (~30m) Digital Terrain Model (DTM) built using a machine-learning-based data fusion approach. This dataset was generated using a global-to-local random forest model trained on ICEsat-2 and GEDI data, leveraging almost 30 billion of the highest-quality elevation points. GEDTM30 is also used to generate 15 land surface parameters at six scales (30, 60, 120, 240, 480 and 960m), covering aspects of topographic position, light and shadow, landform characteristics, and hydrology. A publication describing methods used has been submitted to PeerJ and is in review. The repository demonstrates the modeling and parametrization. Data Components:  • GEDTM30: Terrain Height Prediction - Represents the predicted terrain height  • Uncertainty Map of Terrain Prediction - Provides an uncertainty map of the terrain prediction, derived from the standard deviation of individual tree predictions in the Random Forest model Produced by DTM parametrization, representing different terrain features. Metadata of each parameter is currently stored at scale.csv. The optimized Equi7 tiling system for parameterization is currently stored at equi7_tiles:  • Landform - Slope in Degree, Geomorphons  • Light and Shadow - Positive Openness, Negative Openness, Hillshade  • Curvature - Minimal Curvature, Maximal Curvature, Profile Curvature, Tangential Curvature, Ring Curvature, Shape Index  • Local Topographic Position - Difference from Mean Elevation, Spherical Standard Deviation of the Normals  • Hydrology - Specific Catchment Area, LS Factor, Topographic Wetness Index… This dataset is designed for researchers, developers, and professionals working in earth sciences, GIS, and remote sensing. It can be integrated into various geospatial analysis workflows to enhance terrain representation and modeling accuracy. This dataset covers the entire world and is well-suited for applications in: Topography | Hydrology | Geomorphometry | Others…” #GIS #spatial #mapping #opendata #global #DTM #GEDTM30 #GitHub #30m #landsurface #parameters #documentation #hydrology #remotesensing #earthobservation #slope #geomorphons #hillshade #geomorphometry #geomorphology #topography #Equi7 #terrainheight #DigitalTerrainModel #landform