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==========================================
Rasterio: access to geospatial raster data
==========================================
Geographic information systems use GeoTIFF and other formats to organize and
store gridded raster datasets such as satellite imagery and terrain models.
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 shapes of a raster's valid
data footprint.
.. code:: python
import rasterio
import rasterio.features
import rasterio.warp
with rasterio.open('example.tif') as dataset:
# Read the dataset's valid data mask as a ndarray.
mask = dataset.dataset_mask()
# Extract feature shapes and values from the array.
for geom, val in rasterio.features.shapes(
mask, transform=dataset.transform):
# Transform shapes from the dataset's own coordinate
# reference system to CRS84 (EPSG:4326).
geom = rasterio.warp.transform_geom(
dataset.crs, 'EPSG:4326', geom, precision=6)
# Print GeoJSON shapes to stdout.
print(geom)
The output of the program:
.. code:: python
{'type': 'Polygon', 'coordinates': [[(-77.730817, 25.282335), ...]]}
Rasterio supports Python versions 3.12 or higher.
.. toctree::
:maxdepth: 2
intro
installation
quickstart
cli
topics/index
Rasterio API Reference <api/index>
contributing
faq
Indices and Tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
.. _GDAL: http://gdal.org/
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