rio-tiler 1.0a3

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

Rasterio plugin to serve tiles from AWS S3 hosted files.

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.