User friendly Rasterio plugin to read raster datasets.
Source Code: https://github.com/cogeotiff/rio-tiler
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,
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).
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
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
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("my.tif") 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)
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 .
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.
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.
Create Mapbox Vector Tiles from raster sources
rio-viz: Visualize Cloud Optimized GeoTIFFs locally in the browser
titiler: A lightweight Cloud Optimized GeoTIFF dynamic tile server.
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.