Get mercator tile from CloudOptimized GeoTIFF and other cloud hosted raster such as CBERS-4, Sentinel-2, Sentinel-1 and Landsat-8 AWS PDS
Project description
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)
Starting with version 2.0 rio-tiler only supports Python>=3.
Install
You can install rio-tiler using pip
$ pip install -U pip
$ pip install rio-tiler --pre # version 2.0 is in development
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 io submodules: cogeo, sentinel2, sentinel1, landsat8, cbers.
The rio_tiler.io.cogeo 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.io import cogeo
tile, mask = cogeo.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 render
buffer = render(tile, mask=mask) # this returns a buffer (PNG by default)
Rescale non-byte data and apply colormap
from rio_tiler.colormap import cmap
from rio_tiler.utils import linear_rescale
# Rescale the tile array only where mask is valid and cast it to byte
tile = numpy.where(
mask, linear_rescale(tile, in_range=(0, 1000), out_range=[0, 255]), 0
).astype(numpy.uint8)
cm = cmap.get("viridis")
buffer = render(tile, mask=mask, colormap=cm)
Use creation options to match mapnik default
from rio_tiler.utils import render
from rio_tiler.profiles import img_profiles
options = img_profiles["webp"]
buffer = render(tile, mask=mask, img_format="webp", **options)
Write image to file
with open("my.png", "wb") as f:
f.write(buffer)
Read Sentinel2 tile
from rio_tiler.io import sentinel2
tile, mask = sentinel2.tile('S2A_L1C_20170729_19UDP_0', 77, 89, 8)
print(tile.shape)
> (3, 256, 256)
Use Landsat submodule
from rio_tiler.io 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.io import landsat8
landsat8.metadata('LC08_L1TP_016037_20170813_20170814_01_RT', pmin=5, pmax=95)
{
'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT',
'bounds':(-81.30844102941015, 32.105321365706104, -78.82036599673634, 34.22863519772504),
'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.
Working with SpatioTemporal Asset Catalog (STAC)
In rio-tiler v2, we added a rio_tiler.io.stac submodule to allow tile/metadata fetching of assets withing a STAC item.
from typing import Dict
from rio_tiler.io import stac as STACReader
item: Dict = ... # a STAC Item
# Name of assets to read
assets = ["red", "green", "blue"]
tile, mask = STACReader.tile(item, assets, x, y, z, tilesize=256)
print(tile.shape)
> (3, 256, 256)
Working with multiple assets
rio_tiler.reader submodule has multi_* functions (tile, preview, point, metadata) allowing to fetch and merge info
from multiple dataset (think about multiple bands stored in separated files).
from typing import Dict
from rio_tiler import reader
assets = ["b1.tif", "b2.tif", "b3.tif"]
tile, mask = reader.multi_tile(assets, x, y, z, tilesize=256)
print(tile.shape)
> (3, 256, 256)
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.
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-python3.7
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
Implementations
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]
Python3.7 only
This repo is set to use pre-commit to run isort, flake8, pydocstring, black ("uncompromising Python code formatter") and mypy when committing 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.
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