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 initialy designed to create slippy map tiles from large raster data sources and render these tiles dynamically on a web map. With 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 files and via HTTP, AWS S3, Google Cloud Storage, etc.

At the low level, rio-tiler is just a wrapper around the rasterio.vrt.WarpedVRT class, which can be useful for doing reprojection and/or property overriding (e.g nodata value).

Features

Read any dataset supported by GDAL/Rasterio

from rio_tiler.io import COGReader

with COGReader("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 COGReader

with COGReader("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 assignement (e.g nodata) on data reading

from rio_tiler.io import COGReader

with COGReader("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(x, y, z, assets="asset1", indexes=(1, 2, 3))  # read tile for asset1 and indexes 1,2,3
    img = stac.tile(x, y, z, assets=("asset1", "asset2", "asset3",), indexes=(1,))  # create an image from assets 1,2,3 using their first band

Mosaic (merging or stacking)

from rio_tiler.io import COGReader
from rio_tiler.mosaic import mosaic_reader

def reader(file, x, y, z, **kwargs):
    with COGReader(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 COGReader

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

Install

You can install rio-tiler using pip

$ pip install -U pip
$ pip install -U rio-tiler

or install from source:

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

GDAL>=3.0 / PROJ>=6.0 performances issue

rio-tiler is often used for dynamic tiling, where we need to perform small tasks involving cropping and reprojecting the input data. Starting with GDAL>=3.0 the project shifted to PROJ>=6, which introduced new ways to store projection metadata (using a SQLite database and/or cloud stored grids). This change introduced a performance regression as mentioned in https://mapserver.gis.umn.edu/id/development/rfc/ms-rfc-126.html:

using naively the equivalent calls proj_create_crs_to_crs() + proj_trans() would be a major performance killer, since proj_create_crs_to_crs() can take a time in the order of 100 milliseconds in the most complex situations.

We believe the issue reported in issues/346 is in fact due to ☝️.

To get the best performances out of rio-tiler we recommend for now to use GDAL 2.4 until a solution can be found in GDAL or in PROJ.

Note: Starting with rasterio 1.2.0, rasterio's wheels are distributed with GDAL 3.2 and thus we recommend using rasterio==1.1.8 if using the default wheels, which include GDAL 2.4.

Links:

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

rio-viz: Visualize Cloud Optimized GeoTIFFs locally in the browser

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

jseppi / rio-tiler