rio-tiler

User friendly Rasterio plugin to read raster datasets.

Documentation: https://cogeotiff.github.io/rio-tiler/

Source Code: https://github.com/cogeotiff/rio-tiler

Description

rio-tiler was initially designed to create slippy map tiles from large raster data sources and render these tiles dynamically on a web map. Since rio-tiler v2.0, we added many more helper methods to read data and metadata from any raster source supported by Rasterio/GDAL. This includes local and remote files via HTTP, AWS S3, Google Cloud Storage, etc.

At the low level, rio-tiler is just a wrapper around the rasterio and GDAL libraries.

Features

Read any dataset supported by GDAL/Rasterio

from rio_tiler.io import Reader

with Reader("my.tif") as image:
    print(image.dataset)  # rasterio opened dataset
    img = image.read()    # similar to rasterio.open("my.tif").read() but returns a rio_tiler.models.ImageData object

User friendly tile, part, feature, point reading methods

from rio_tiler.io import Reader

with Reader("my.tif") as image:
    img = image.tile(x, y, z)            # read mercator tile z-x-y
    img = image.part(bbox)               # read the data intersecting a bounding box
    img = image.feature(geojson_feature) # read the data intersecting a geojson feature
    img = image.point(lon,lat)           # get pixel values for a lon/lat coordinates

Enable property assignment (e.g nodata) on data reading

from rio_tiler.io import Reader

with Reader("my.tif") as image:
    img = image.tile(x, y, z, nodata=-9999) # read mercator tile z-x-y

STAC support

from rio_tiler.io import STACReader

with STACReader("item.json") as stac:
    print(stac.assets)  # available asset
    img = stac.tile(  # read tile for asset1 and indexes 1,2,3
        x,
        y,
        z,
        assets="asset1",
        indexes=(1, 2, 3),  # same as asset_indexes={"asset1": (1, 2, 3)},
    )

    # Merging data from different assets
    img = stac.tile(  # create an image from assets 1,2,3 using their first band
        x,
        y,
        z,
        assets=("asset1", "asset2", "asset3",),
        asset_indexes={"asset1": 1, "asset2": 1, "asset3": 1},
    )

Xarray support (>=4.0)

import xarray
from rio_tiler.io import XarrayReader

ds = xarray.open_dataset(
    "https://pangeo.blob.core.windows.net/pangeo-public/daymet-rio-tiler/na-wgs84.zarr/",
    engine="zarr",
    decode_coords="all",
    consolidated=True,
)
da = ds["tmax"]
with XarrayReader(da) as dst:
    print(dst.info())
    img = dst.tile(1, 1, 2)

Note: The XarrayReader needs optional dependencies to be installed pip install rio-tiler["xarray"].

Non-Geo Image support (>=4.0)

from rio_tiler.io import ImageReader

with ImageReader("image.jpeg") as src:
    im = src.tile(0, 0, src.maxzoom)  # read top-left `tile`
    im = src.part((0, 100, 100, 0))  # read top-left 100x100 pixels
    pt = src.point(0, 0)  # read pixel value

Note: ImageReader is also compatible with proper geo-referenced raster datasets.

Mosaic (merging or stacking)

from rio_tiler.io import Reader
from rio_tiler.mosaic import mosaic_reader

def reader(file, x, y, z, **kwargs):
    with Reader(file) as image:
        return image.tile(x, y, z, **kwargs)

img, assets = mosaic_reader(["image1.tif", "image2.tif"], reader, x, y, z)

Native support for multiple TileMatrixSet via morecantile

import morecantile
from rio_tiler.io import Reader

# Use EPSG:4326 (WGS84) grid
wgs84_grid = morecantile.tms.get("WorldCRS84Quad")
with Reader("my.tif", tms=wgs84_grid) as src:
    img = src.tile(1, 1, 1)

Install

You can install rio-tiler using pip

python -m pip install -U pip
python -m pip install -U rio-tiler

or install from source:

git clone https://github.com/cogeotiff/rio-tiler.git
cd rio-tiler
python -m pip install -U pip
python -m pip install -e .

Plugins

rio-tiler-pds

rio-tiler v1 included several helpers for reading popular public datasets (e.g. Sentinel 2, Sentinel 1, Landsat 8, CBERS) from cloud providers. This functionality is now in a separate plugin, enabling easier access to more public datasets.

rio-tiler-mvt

Create Mapbox Vector Tiles from raster sources

Implementations

titiler: A lightweight Cloud Optimized GeoTIFF dynamic tile server.

cogeo-mosaic: Create mosaics of Cloud Optimized GeoTIFF based on the mosaicJSON specification.

Contribution & Development

See CONTRIBUTING.md

Authors

The rio-tiler project was begun at Mapbox and was transferred to the cogeotiff Github organization in January 2019.

See AUTHORS.txt for a listing of individual contributors.

Changes

See CHANGES.md.

License

See LICENSE

Metadata

Release files for rio-tiler 9.4.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for rio-tiler 9.4.6
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Built distribution (wheel)

Table of built distributions (wheels) for rio-tiler 9.4.6
File	Interpreter	ABI	Platform	Reset
rio_tiler-9.4.6-py3-none-any.whl 316.8 kB Sep 16, 2026	Python 3	none	any	Details

Total release size: 529.8 kB

Release files / rio_tiler-9.4.6.tar.gz

Download URL	rio_tiler-9.4.6.tar.gz
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Release files / rio_tiler-9.4.6-py3-none-any.whl

Download URL	rio_tiler-9.4.6-py3-none-any.whl
Size	316.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`4918a91c4e61f0165aa7d6325f09613b90f2d75791ea9fb0666daaa4111033e4`
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Upload date	Sep 16, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/cogeotiff/rio-tiler
Publishing commit: github.com/cogeotiff/rio-tiler/tree/d6ef78092aff8741a2dbb5ff2b5fe1d055cc3456

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Publishing configuration: github.com/cogeotiff/rio-tiler/blob/d6ef78092aff8741a2dbb5ff2b5fe1d055cc3456/.github/workflows/release.yml
Publishing logs: github.com/cogeotiff/rio-tiler/actions/runs/35114080007/attempts/1

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Subject: rio_tiler-9.4.6-py3-none-any.whl
SHA-256 checksum: 4918a91c4e61f0165aa7d6325f09613b90f2d75791ea9fb0666daaa4111033e4 Verifying attestations