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)

• 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-pds]

[rio-tiler-pds]: https://github.com/cogeotiff/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][rio-tiler-pds], enabling easier access to more public datasets.

[rio-tiler-mvt][rio-tiler-mvt]

Create Mapbox Vector Tiles from raster sources

[rio-tiler-mvt]: https://github.com/cogeotiff/rio-tiler-mvt

Implementations

[titiler][titiler]: A lightweight Cloud Optimized GeoTIFF dynamic tile server.

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

[titiler]: https://github.com/developmentseed/titiler [cogeo-mosaic]: https://github.com/developmentseed/cogeo-mosaic [mosaicjson_spec]: https://github.com/developmentseed/mosaicjson-spec

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