rio-tiler 1.4.0

Get mercator tile from CloudOptimized GeoTIFF and other cloud hosted raster such as CBERS-4, Sentinel-2, Sentinel-1 and Landsat-8 AWS PDS

Rio-tiler

Rasterio plugin to read mercator tiles from Cloud Optimized GeoTIFF dataset.

Additional support is provided for the following satellite missions hosted on AWS Public Dataset:

• Sentinel2 (requester-pays) (please read this)
• Sentinel1 (requester-pays)
• Landsat8
• CBERS (requester-pays)

Rio-tiler supports Python 2.7 and 3.3-3.7.

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/cogeotiff/rio-tiler.git
$ cd rio-tiler
$ pip install -U pip
$ pip install -e .

Overview

Create tiles using one of these rio_tiler modules: main, sentinel2, sentinel1, landsat8, cbers.

The main module can create mercator tiles from any raster source supported by Rasterio (i.e. local files, http, s3, gcs 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.

Each tilling modules have a method to return image metadata (e.g bounds).

Usage

Read a tile from a file over the internet

from rio_tiler import main

tile, mask = main.tile(
  'http://oin-hotosm.s3.amazonaws.com/5a95f32c2553e6000ce5ad2e/0/10edab38-1bdd-4c06-b83d-6e10ac532b7d.tif',
  691559,
  956905,
  21,
  tilesize=256
)
print(tile.shape)
> (3, 256, 256)

print(mask.shape)
> (256, 256)

Create image from tile

from rio_tiler.utils import array_to_image

buffer = array_to_image(tile, mask=mask) # this returns a buffer (PNG by default)

Use creation options to match mapnik default

from rio_tiler.utils import array_to_image
from rio_tiler.profiles import img_profiles

options = img_profiles["webp"]
buffer = array_to_image(tile, mask=mask, img_format="webp", **options)

Write image to file

with open("my.png", "wb") as f:
  f.write(buffer)

Get a Sentinel2 tile and its nodata mask.

from rio_tiler import sentinel2

tile, mask = sentinel2.tile('S2A_tile_20170729_19UDP_0', 77, 89, 8)
print(tile.shape)
> (3, 256, 256)

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. percentiles (pc) min/max values, histograms, and bounds in WGS84) .

from rio_tiler import landsat8

landsat8.metadata('LC08_L1TP_016037_20170813_20170814_01_RT', pmin=5, pmax=95)
{
  'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT',
  'bounds': {
    'value': (-81.30844102941015, 32.105321365706104,  -78.82036599673634, 34.22863519772504),
    'crs': '+init=EPSG:4326'
  },
  'statistics': {
    '1': {
      'pc': [1251.297607421875, 5142.0126953125],
      'min': -1114.7020263671875,
      'max': 11930.634765625,
      'std': 1346.6463388957156,
      'histogram': [
        [1716, 257951, 174296, 36184, 20828, 11783, 6862, 2941, 635, 99],
        [-1114.7020263671875, 189.83164978027344, 1494.3653564453125, 2798.89892578125, 4103.4326171875, 5407.96630859375, 6712.5, 8017.03369140625, 9321.5673828125, 10626.1015625, 11930.634765625]
      ]
    },
    ...
    ...
    '11': {
      'pc': [278.3393859863281, 293.4466247558594],
      'min': 147.27650451660156,
      'max': 297.4621276855469,
      'std': 7.660112832018338,
      'histogram': [
        [207, 201, 204, 271, 350, 944, 1268, 2383, 43085, 453084],
        [147.27650451660156, 162.29507446289062, 177.31362915039062, 192.33218383789062, 207.3507537841797, 222.36932373046875, 237.38787841796875, 252.40643310546875, 267.42498779296875, 282.4435729980469, 297.4621276855469]
      ]
    }
  }
}

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.

Requester-pays Buckets

On AWS, sentinel2, sentinel1, and cbers dataset are stored in a requester-pays bucket, meaning the cost of GET, LIST requests will be charged to the users. For rio-tiler to work with those buckets, you'll need to set AWS_REQUEST_PAYER="requester" in your environement.

Partial reading on Cloud hosted dataset

Rio-tiler perform partial reading on local or distant dataset, which is why it will perform best on Cloud Optimized GeoTIFF (COG). It's important to note that Sentinel-2 scenes hosted on AWS are not in Cloud Optimized format but in JPEG2000. When performing partial reading of JPEG2000 dataset GDAL (rasterio backend library) will need to make a lot of GET requests and transfer a lot of data.

Ref: Do you really want people using your data blog post.

Contribution & Development

Issues and pull requests are more than welcome.

dev install

$ git clone https://github.com/cogeotiff/rio-tiler.git
$ cd rio-tiler
$ pip install -e .[dev]

Python3.6 only

This repo is set to use pre-commit to run flake8, pydocstring and black ("uncompromising Python code formatter") when commiting new code.

$ pre-commit install

License

See LICENSE.txt

Authors

The rio-tiler project was begun at Mapbox and has been transferred in January 2019.

See AUTHORS.txt for a listing of individual contributors.

Changes

See CHANGES.txt.

Create an AWS Lambda package

The easiest way to make sure the package will work on AWS is to use docker

FROM lambci/lambda:build-python3.6

ENV LANG=en_US.UTF-8 LC_ALL=en_US.UTF-8 CFLAGS="--std=c99"

RUN pip3 install rio-tiler --no-binary numpy -t /tmp/python -U

RUN cd /tmp/python && zip -r9q /tmp/package.zip *

Ref: https://github.com/vincentsarago/simple-rio-lambda

Plugins

• rio-tiler-mosaic
• rio-tiler-mvt

Implementations

• remotepixel-tiler
• CosmiQ/solaris
• lambda-tiler
• cogeo-mosaic