Get mercator tile from landsat, sentinel or other AWS hosted raster
Clone or download
Latest commit 9aa45d6 Aug 22, 2018
Permalink
Failed to load latest commit information.
.circleci update CI tests Jun 20, 2018
rio_tiler version bump Aug 22, 2018
tests add pix4d alpha nodata test case Aug 22, 2018
.gitignore update gitignore Mar 26, 2018
AUTHORS.txt init Oct 9, 2017
CHANGES.txt version bump Aug 22, 2018
LICENSE.txt init Oct 9, 2017
MANIFEST.in make sure we ship the colormaps Nov 23, 2017
README.rst version update May 14, 2018
codecov.yml init Oct 9, 2017
requirements.txt rasterio 1.0.0 Jul 16, 2018
setup.py version bump Aug 22, 2018
tox.ini update from master Jun 22, 2018

README.rst

Rio-tiler

Rasterio plugin to create mercator tiles from raster sources.

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

Additional support is provided for the following satellite missions:

  • Sentinel 2
  • Landsat 8
  • CBERS

Rio-tiler supports Python 2.7 and 3.3-3.6.

Install

You can install rio-tiler using pip

$ 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 .

here is how to create an AWS Lambda package on most UNIX machines:

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

Overview

Create tiles using one of these rio_tiler modules: main, sentinel2, landsat8, cbers.

The main module can create mercator tiles from any raster source supported by Rasterio (i.e. local files, http, etc.). The mission specific modules make it easier to extract tiles from AWS S3 buckets (i.e. only a scene ID is required); They can also be used to return metadata.

All of the tiling modules can return the original image bounds.

Usage

Get a Sentinel2 tile and its mask (if any).

from rio_tiler import sentinel2
tile, mask = sentinel2.tile('S2A_tile_20170729_19UDP_0', 77, 89, 8)
tile.shape
# (3, 256, 256)

Create image from tile

from rio_tiler.utils import array_to_img
img = array_to_img(tile, mask=mask) # this returns a pillow image

Convert image into base64 encoded string (PNG or JPEG)

from rio_tiler.utils import b64_encode_img
str_img = b64_encode_img(img, 'jpeg')

Get bounds for a Landsat scene (WGS84).

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'}

Get metadata of a Landsat scene (i.e. percentinle min and max values, and bounds in WGS84) .

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'}

The primary purpose for calculating minimum and maximum values of an image is to rescale pixel values from their original range (e.g. 0 to 65,535) to the range used by computer screens (i.e. 0 and 255) through a linear transformation. This will make images look good on display.

The Datasets

License

See LICENSE.txt.

Authors

See AUTHORS.txt.

Changes

See CHANGES.txt.