The data is stored in commonly used text and GIS file formats, such as comma-separated values (CSV), ESRI shapefiles, and GeoTIFF. We georeferenced all files to the INSPIRE coordinate reference system
Machine learning has indeed become an important method for gully erosion modeling, but its accuracy in simulating gully density remains significantly lower than that of gully erosion susceptibility as
S1: DEM preparation and terrain indice modeling, Python Code and DEM This folder contains all data needed to produce the terrain indices modeled within the paper. It contains the Python Code for DEM
Spatial input data to parameterise the gully erosion module of the dSedNet model to simulate sediment generation and transport in the Western Port catchment for a 2018-19 study commissioned by Melbour
The data is stored in commonly used text and GIS file formats, such as comma-separated values (CSV), ESRI shapefiles, and GeoTIFF. We georeferenced all files to the INSPIRE coordinate reference system