Rio-Tiler-PDS: A rio-tiler plugin for Public Datasets

A rio-tiler plugin to access and read Public hosted datasets.

Important This is the new module for rio-tiler missions specific (ref: https://github.com/cogeotiff/rio-tiler/issues/195)

Documentation: https://cogeotiff.github.io/rio-tiler-pds/

Source Code: https://github.com/cogeotiff/rio-tiler-pds

Installation

You can install rio-tiler-pds using pip

$ pip install -U pip
$ pip install rio-tiler-pds

or install from source:

$ git clone https://github.com/cogeotiff/rio-tiler-pds.git
$ cd rio-tiler-pds
$ pip install -U pip
$ pip install -e .

Datasets

Adding more dataset:

If there is a dataset that can easily be described with a scene id please feel free to Open an Issue or a PR directly.

Warnings

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.

Overview

Readers

Each dataset has its own submodule (e.g sentinel2: rio_tiler_pds.sentinel.aws)

from rio_tiler_pds.landsat.aws import L8Reader
from rio_tiler_pds.sentinel.aws import S1L1CReader
from rio_tiler_pds.sentinel.aws import (
    S2L1CReader,  # JPEG2000
    S2L2AReader,  # JPEG2000
    S2L2ACOGReader,   # COG
)
from rio_tiler_pds.cbers.aws import CBERSReader

All Readers are subclass of rio_tiler.io.BaseReader and inherit its properties/methods.

Properties

• bounds: Scene bounding box
• minzoom: WebMercator MinZoom (e.g 7 for Landsat8)
• maxzoom: WebMercator MaxZoom (e.g 12 for Landsat8)
• center: Scene center
• spatial_info: zooms, bounds and center

Methods

• info: Returns asset's (band) simple info (e.g nodata, band_descriptions, ....)
• stats: Returns asset's statistics (percentile, histogram, ...)
• metadata: info + stats
• tile: Read web mercator map tile from assets (bands)
• part: Extract part of assets (bands)
• preview: Returns a low resolution preview from assets (bands)
• point: Returns asset's pixel value for a given lon,lat

Other

• assets: List of available assets (bands) for each dataset

Scene ID

All readers take scene id as main input. The scene id is used internaly by the reader to derive the full path of the data.

e.g: Landsat on AWS

Because the Landsat AWS PDS follows a regular schema to store the data (s3://{bucket}/c1/L8/{path}/{row}/{scene}/{scene}_{asset}.TIF"), we can easily reconstruct the full asset's path by parsing the scene id.

from rio_tiler_pds.landsat.aws import L8Reader
from rio_tiler_pds.landsat.utils import sceneid_parser

sceneid_parser("LC08_L1TP_016037_20170813_20170814_01_RT")

> {
  'sensor': 'C',
  'satellite': '08',
  'processingCorrectionLevel': 'L1TP',
  'path': '016',
  'row': '037',
  'acquisitionYear': '2017',
  'acquisitionMonth': '08',
  'acquisitionDay': '13',
  'processingYear': '2017',
  'processingMonth': '08',
  'processingDay': '14',
  'collectionNumber': '01',
  'collectionCategory': 'RT',
  'scene': 'LC08_L1TP_016037_20170813_20170814_01_RT',
  'date': '2017-08-13'
}

with L8Reader("LC08_L1TP_016037_20170813_20170814_01_RT") as landsat:
    print(landsat._get_asset_url("B1"))

> s3://landsat-pds/c1/L8/016/037/LC08_L1TP_016037_20170813_20170814_01_RT/LC08_L1TP_016037_20170813_20170814_01_RT_B1.TIF

Each Dataset have their specific scene id format:

File Per Band

rio-tiler-pds Readers assume that assets (eo:band) are stored in separate files.

$ aws s3 ls landsat-pds/c1/L8/013/031/LC08_L1TP_013031_20130930_20170308_01_T1/

LC08_L1TP_013031_20130930_20170308_01_T1_B1.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B10.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B11.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B2.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B3.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B4.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B5.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B6.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B7.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B8.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_B9.TIF
LC08_L1TP_013031_20130930_20170308_01_T1_BQA.TIF

When reading data or metadata, readers will merge them.

e.g

with S2L1CReader("S2A_L1C_20170729_19UDP_0") as sentinel:
    tile, mask = sentinel.tile(77, 89, 8, assets=("B01", "B02")
    assert tile.shape == (2, 256, 256)

    print(sentinel.stats(assets=("B8A", "B02")))
    > {
      'B8A': {
        'pc': [106, 9322],
        'min': 1,
        'max': 13659,
        'std': 2682.6511198930048,
        'histogram': [
          [261631, 52188, 137746, 98039, 41066, 30818, 21095, 8631, 1442, 105],
          [1.0, 1366.8, 2732.6, 4098.4, 5464.2, 6830.0, 8195.8, 9561.6, 10927.4, 12293.199999999999, 13659.0]
        ]
      },
      'B02': {
        ...
      }
    }

Changes

See CHANGES.md.

Contribution & Development

See CONTRIBUTING.md

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.