Results 11 to 20 of about 95,896 (348)

Multiscale analysis of surface roughness for the improvement of natural hazard modelling [PDF]

open access: yesNatural Hazards and Earth System Sciences, 2021
Surface roughness influences the release of avalanches and the dynamics of rockfall, avalanches and debris flow, but it is often not objectively implemented in natural hazard modelling.
N. Brožová   +8 more
doaj   +1 more source

TERRA: Terrain Extraction from elevation Rasters through Repetitive Anisotropic filtering

open access: yesInternational Journal of Applied Earth Observations and Geoinformation, 2020
Over the past decades, several filters have been developed to derive a Digital Terrain Model (DTM) from a Digital Surface Model (DSM), by means of filtering out aboveground objects such as vegetation.
Anton Pijl   +5 more
doaj   +1 more source

BARE-EARTH EXTRACTION AND DTM GENERATION FROM PHOTOGRAMMETRIC POINT CLOUDS WITH A PARTIAL USE OF AN EXISTING LOWER RESOLUTION DTM [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
A method of extracting bare-earth points from photogrammetric point clouds by partially using an existing lower resolution digital terrain model (DTM) is presented. The bare-earth points are extracted based on a threshold defined by local slope.
M. Debella-Gilo
doaj   +1 more source

INTEGRATION OF 3D OBJECTS AND TERRAIN FOR 3D MODELLING SUPPORTING THE DIGITAL TWIN [PDF]

open access: yesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
3D modelling of precincts and cities has significantly advanced in the last decades, as we move towards the concept of the Digital Twin. Many 3D city models have been created but a large portion of them neglect representing terrain and buildings ...
J. Yan   +4 more
doaj   +1 more source

UAV-Based Terrain Modeling under Vegetation in the Chinese Loess Plateau: A Deep Learning and Terrain Correction Ensemble Framework

open access: yesRemote Sensing, 2020
Accurate topographic mapping is a critical task for various environmental applications because elevation affects hydrodynamics and vegetation distributions.
Jiaming Na   +6 more
doaj   +1 more source

Digital Terrain Model Geospatial Modelling

open access: yesIOP Conference Series: Earth and Environmental Science, 2021
Abstract The modelling means the world object cognition based on the analogy. This analogy presents an idea and material imitation of some properties of the existing world. It is processed by various anthropogenic objects, in which the chosen properties are presented, defined and characterised as shapes and relations of original objects.
Robert Sasik   +2 more
openaire   +1 more source

Digital terrain model height estimation using support vector machine regression

open access: yesSouth African Journal of Science, 2015
Digital terrain model interpolation is intrinsically a surface fitting problem, in which unknown heights H are estimated from known X-Y coordinates.
Onuwa Okwuashi, Christopher Ndehedehe
doaj   +1 more source

FOSS4G date assessment on the isprs optical stereo satellite data. A benchmark for DSM generation [PDF]

open access: yes, 2017
The ISPRS Working Group 4 Commission I on "Geometric and Radiometric Modelling of Optical Spaceborne Sensors", provides a benchmark dataset with several stereo data sets from space borne stereo sensors.
Crespi, Mattia   +2 more
core   +1 more source

Modelling the spatial distribution of DEM Error [PDF]

open access: yes, 2005
Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation.
Bolstad P V   +23 more
core   +1 more source

THE EXPLAINABILITY OF GRADIENT-BOOSTED DECISION TREES FOR DIGITAL ELEVATION MODEL (DEM) ERROR PREDICTION [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023
Gradient boosted decision trees (GBDTs) have repeatedly outperformed several machine learning and deep learning algorithms in competitive data science.
C. Okolie   +6 more
doaj   +1 more source

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