User friendly Rasterio plugin to read raster datasets.

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

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

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.

• 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)

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 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

titiler: A lightweight Cloud Optimized GeoTIFF dynamic tile server.

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

See CONTRIBUTING.md

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.

See CHANGES.md.

See LICENSE