Raster4ML: A geospatial raster processing library for machine learning
Raster4ML is a python package that extracts machine learning ready dataset from geospatial raster data and shapefiles. The package aims to aid geospatial researchers and scientists to extract meaningful faetures easily and focus more on the model training or reproducibility issues.
Key Features
Stack raster bands
Automatically calculate vegetation indices (supports 350+ indices)
Extract raster values based on shapefile
Clip raster based on polygon
Raster4ML works with raster data derived from satellites, airplanes or UAVs. The applications can be supported in precision agriculture, plant phenotyping, hydrology, natural resource management, and other fields within the geospatial science.
Here is an example of creating as many vegetation index as the image wavelenghts support by simply using the following function:
from raster4ml.features import VegetaionIndices
VI = VegetationIndices(image_path='Landsat8.tif',
wavelengths=[442.96, 482.04, 561.41, 654.59, 864.67, 1608.86, 2200.73])
VI.calculate(out_dir='vegetation_indices')