/rio-tiler

Get mercator tile from landsat, sentinel or other AWS hosted raster

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

Rio-tiler

Rasterio plugin to serve tiles from AWS S3 hosted files.

https://circleci.com/gh/mapbox/rio-tiler.svg?style=svg&circle-token=b78bc1a238c21046a855a9c80b441a8f2f9a4478 https://codecov.io/gh/mapbox/rio-tiler/branch/master/graph/badge.svg?token=zuHupC20cG

Get mercator tile from Landsat, sentinel or other AWS hosted rasters.

Rio-tiler supports Python 2.7 and 3.3-3.6.

Install

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

Or install from source:

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

Or if you want to create an AWS Lambda package using rasterio wheels:

# On a centos machine
$ pip install rio-tiler --no-binary numpy -t /tmp/vendored -U
$ zip -r9q package.zip vendored/*

API Overview

rio_tiler.landsat8

The landsat8 module processes data hosted on the Landsat 8 AWS Public Dataset.

  • landsat8.bounds

    Get WGS84 bounds for a Landsat scene.

    Input:
    • sceneid: Landsat product id (or scene id for scene < 1st May 2017)
    Output:
    • dictionary:
      • bounds: (minX, minY, maxX, maxY) (list)
      • sceneid: scene id (string)
    >>> from rio_tiler import landsat8
>>> landsat8.bounds('LC08_L1TP_016037_20170813_20170814_01_RT')
{'bounds': [-81.30836, 32.10539, -78.82045, 34.22818],
'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT'}

  • landsat8.metadata

    Get WGS84 bounds and cumulative histogram cuts for each bands for a Landsat scene.

    Input:
    • sceneid: Landsat product id (or scene id for scene < 1st May 2017)
    • pmin: Histogram cut minimum value in percent (default: 2)
    • pmax: Histogram cut maximum value in percent (default: 98)
    Output:
    • dictionary:
      • bounds: (minX, minY, maxX, maxY) (list)
      • sceneid: scene id (string)
      • rgbMinMax: Min/Max DN values for the linear rescaling (dictionary)
      >>> from rio_tiler import landsat8
>>> landsat8.metadata('LC08_L1TP_016037_20170813_20170814_01_RT', pmin=5, pmax=95)
{'bounds': [-81.30836, 32.10539, -78.82045, 34.22818],
 'rgbMinMax': {'1': [1245, 5396],
  '2': [983, 5384],
  '3': [718, 5162],
  '4': [470, 5273],
  '5': [403, 6440],
  '6': [258, 4257],
  '7': [151, 2984]},
 'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT'}

  • landsat8.tile

    Return base64 encoded image corresponding to a mercator tile

    Input:
    • sceneid : Landsat product id (or scene id for scene < 1st May 2017)
    • x: Mercator tile X index
    • y: Mercator tile Y index
    • z: Mercator tile ZOOM level
    • rgb: Bands index for the RGB combination (default: (4, 3, 2))
    • tilesize: Output image size (default: 256)
    • pan: If True, apply pan-sharpening (default: False)
    Output:
    • numpy ndarray of the image data
    >>> from rio_tiler import landsat8
>>> tile = landsat8.tile('LC08_L1TP_016037_20170813_20170814_01_RT', 71, 102, 8)
>>> tile.shape
(3, 256, 256)

rio_tiler.sentinel2

The sentinel2 module processes data hosted on the Sentinel 2 AWS Public Dataset.

  • sentinel2.bounds

    Get WGS84 bounds for a Landsat scene.

    Input:
    • sceneid: Sentinel scene id (S2{A|B}_tile_{YYYYMMDD}_{utm_zone}{latitude_band}{grid_square}_{img_number})
    Output:
    • dictionary:
      • bounds: (minX, minY, maxX, maxY) (list)
      • sceneid: scene id (string)
    >>> from rio_tiler import sentinel2
>>> sentinel2.bounds('S2A_tile_20170729_19UDP_0')
{'bounds': [-70.36082319774495, 47.75776333620836, -68.8677615795376, 48.75301295078041],
 'sceneid': 'S2A_tile_20170729_19UDP_0'}

  • sentinel2.metadata

    Get WGS84 bounds and cumulative histogram cuts for each bands for a Sentinel scene.

    Input:
    • sceneid: Sentinel scene id (S2{A|B}_tile_{YYYYMMDD}_{utm_zone}{latitude_band}{grid_square}_{img_number})
    • pmin: Histogram cut minimum value in percent (default: 2)
    • pmax: Histogram cut maximum value in percent (default: 98)
    Output:
    • dictionary:
      • bounds: (minX, minY, maxX, maxY) (list)
      • sceneid: scene id (string)
      • rgbMinMax: Min/Max DN values for the linear rescaling (dictionary)
    >>> from rio_tiler import sentinel2
>>> sentinel2.metadata('S2A_tile_20170729_19UDP_0', pmin=5, pmax=95)
{'sceneid': 'S2A_tile_20170729_19UDP_0',
'bounds': [-70.36082319774495, 47.75776333620836, -68.8677615795376, 48.75301295078041],
'rgbMinMax': {
    '01': [1088, 8237],
    '02': [740, 8288],
    '03': [488, 7977],
    '04': [255, 8626],
    '05': [210, 8877],
    '06': [172, 9079],
    '07': [150, 9263],
    '08': [122, 9163],
    '8A': [107, 9360],
    '09': [53, 5926],
    '10': [6, 546],
    '11': [15, 5658],
    '12': [8, 4009]}}

  • sentinel2.tile

    Return base64 encoded image corresponding to a mercator tile

    Input:
    • sceneid : Sentinel scene id (S2{A|B}_tile_{YYYYMMDD}_{utm_zone}{latitude_band}{grid_square}_{img_number})
    • x: Mercator tile X index
    • y: Mercator tile Y index
    • z: Mercator tile ZOOM level
    • rgb: Bands index for the RGB combination (default: (04, 03, 02))
    • tilesize: Output image size (default: 256)
    Output:
    • numpy ndarray of the image data
    >>> from rio_tiler import sentinel2
>>> sentinel2.tile('S2A_tile_20170729_19UDP_0', 77, 89, 8, 'png')
>>> tile.shape
(3, 256, 256)

rio_tiler.aws

The aws module can process any raster hosted on AWS S3.

  • aws.bounds

    Get WGS84 bounds for a scene.

    Input:
    • bucket: AWS S3 bucket name where the raster is stored
    • key: AWS S3 key
    Output:
    • dictionary:
      • bounds: (minX, minY, maxX, maxY) (list)
      • bucket: bucket name
      • key: AWS key
    >>> from rio_tiler import aws
>>> aws.bounds('my-bucket', 'data/my-raster.tif')
{'bounds': [-104.77532797841498, 38.95344940972065, -104.77466477631017, 38.95376633047638],
 'bucket': 'my-bucket'
 'key': 'data/my-raster.tif'}

  • aws.tile

    Return base64 encoded image corresponding to a mercator tile

    Input:
    • bucket: bucket name
    • key: AWS key
    • x: Mercator tile X index
    • y: Mercator tile Y index
    • z: Mercator tile ZOOM level
    • rgb: Band index to read (default: (1, 2, 3))
    • tilesize: Output image size (default: 256)
    Output:
    • numpy ndarray of the image data
    >>> from rio_tiler import aws
>>> aws.tile('my-bucket', 'data/my-raster.tif', 77, 89, 8)
>>> tile.shape
(3, 256, 256)

Convert tile output to image

rio_tiler.utils.array_to_img

Input:
  • numpy nuint8 ndarray
  • tileformat: Image format to return ("jpg" or "png")
Output:
  • base64 encoded image PNG or JPEG (string)
>>> from rio_tiler import landsat8
>>> from rio_tiler.utils import array_to_img
>>> tile = landsat8.tile('LC08_L1TP_016037_20170813_20170814_01_RT', 71, 102, 8)
>>> array_to_img(tile, 'png')
'iVBORw0KGgoAAAANSUhEUgAAAQAAAAEACAYAAABccqhmAAEAAElEQVR4AQAggN9/AAAAAAA....

License

See LICENSE.txt.

Authors

See AUTHORS.txt.

Changes

See CHANGES.txt.