Get mercator tile from landsat, sentinel or other AWS hosted raster
Project description
Rasterio pluggin 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-tilerOr 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 process Landsat 8 data hosted on AWS Public Dataset https://aws.amazon.com/fr/public-datasets/landsat/
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))
r_bds: First band (red) DN min and max values (default: (0, 16000))
g_bds: Second band (green) DN min and max values (default: (0, 16000))
b_bds: Third band (blue) DN min and max values (default: (0, 16000))
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 process Sentinel 2 data hosted on AWS Public Dataset http://sentinel-pds.s3-website.eu-central-1.amazonaws.com
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))
r_bds: First band (red) DN min and max values (default: (0, 16000))
g_bds: Second band (green) DN min and max values (default: (0, 16000))
b_bds: Third band (blue) DN min and max values (default: (0, 16000))
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.
Changes
See CHANGES.txt.
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