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.vrt.WarpedVRT class, which can be useful for doing re-projection and/or property overriding (e.g nodata value).
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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
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User friendly
tile,part,feature,pointreading methodsfrom 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
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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
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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}, )
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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)
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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-tileror 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
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 s 632A pecification.
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