Rasterio reads and writes these formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON. Here’s an example program that extracts the GeoJSON …

This document explains how to use Rasterio to read existing files and to create new files. Some advanced topics are glossed over to be covered in more detail elsewhere in Rasterio’s …

Installation of the Rasterio package is complicated by its dependency on libgdal and other C libraries. There are easy installations paths and an advanced installation path.

Dataset objects provide read, read-write, and write access to raster data files and are obtained by calling rasterio.open(). That function mimics Python’s built-in open() and the dataset objects it …

Rasterio’s goal is to be this kind of raster data library – expressing GDAL’s data model using fewer non-idiomatic extension classes and more idiomatic Python types and protocols, while …

Plotting Rasterio reads raster data into numpy arrays so plotting a single band as two dimensional data can be accomplished directly with pyplot.

rasterio package Subpackages rio CLI rio blocks rio bounds rio calc rio clip Examples rio convert rio edit_info rio env rio gcps rio info rio insp rio mask rio merge rio overview rio rasterize Note …

sharing (bool, optional) – To reduce overhead and prevent programs from running out of file descriptors, rasterio maintains a pool of shared low level dataset handles.

Rasterio can map the pixels of a destination raster with an associated coordinate reference system and transform to the pixels of a source image with a different coordinate reference …

Custom CRS can be described in text using several formats. Rasterio’s CRS class is our abstraction for coordinate reference systems. A rasterio dataset’s crs property is an instance …