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User friendly Rasterio plugin to read raster datasets.

Project description

rio-tiler

rio-tiler

User friendly Rasterio plugin to read raster datasets.

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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.vrt.WarpedVRT class, which can be useful for doing re-projection 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 assignment (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(  # 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},
        )
    
  • 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 .

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

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