Awesome

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

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

Documentation: <a href="https://cogeotiff.github.io/rio-tiler-pds/" target="_blank">https://cogeotiff.github.io/rio-tiler-pds/</a>

Source Code: <a href="https://github.com/cogeotiff/rio-tiler-pds" target="_blank">https://github.com/cogeotiff/rio-tiler-pds</a>

Installation

You can install rio-tiler-pds using pip

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

or install from source:

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

Datasets

Adding more dataset:

If you know of another publicly-available dataset that can easily be described with a "scene id", please feel free to open an issue.

Warnings

Requester-pays Buckets

On AWS, sentinel2, sentinel1, cbers and modis (in astraea-opendata) datasets are stored in requester pays buckets. This means that the cost of GET and LIST requests and egress fees for downloading files outside the AWS region will be charged to the accessing users, not the organization hosting the bucket. For rio-tiler and rio-tiler-pds to work with such buckets, you'll need to set AWS_REQUEST_PAYER="requester" in your shell environment.

Partial reading on Cloud hosted dataset

When reading data, rio-tiler-pds performs partial reads when possible. Hence performance will be best on data stored as 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. Partial reads from JPEG2000 files are inefficient, and GDAL (the library underlying rio-tiler-pds and rasterio) will need to make many GET requests and transfer a lot of data. This will be both slow and expensive, since AWS's JPEG2000 collection of Sentinel 2 data is stored in a requester pays bucket.

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 LandsatC2Reader
from rio_tiler_pds.sentinel.aws import S1L1CReader
from rio_tiler_pds.sentinel.aws import (
    S2JP2Reader,  # JPEG2000
    S2COGReader,   # COG
)

from rio_tiler_pds.cbers.aws import CBERSReader
from rio_tiler_pds.modis.aws import MODISPDSReader, MODISASTRAEAReader
from rio_tiler_pds.copernicus.aws import Dem30Reader, Dem90Reader

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

Properties

• bounds: Scene bounding box
• crs: CRS of the bounding box
• geographic_bounds: bounding box in geographic projection (e.g WGS84)
• minzoom: WebMercator MinZoom (e.g 7 for Landsat 8)
• maxzoom: WebMercator MaxZoom (e.g 12 for Landsat 8)

Methods

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

Other

• bands (property): List of available 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}_{band}.TIF"), we can easily reconstruct the full band's path by parsing the scene id.

from rio_tiler_pds.landsat.aws import LandsatC2Reader
from rio_tiler_pds.landsat.utils import sceneid_parser

sceneid_parser("LC08_L2SP_001062_20201031_20201106_02_T2")

> {'sensor': 'C',
 'satellite': '08',
 'processingCorrectionLevel': 'L2SP',
 'path': '001',
 'row': '062',
 'acquisitionYear': '2020',
 'acquisitionMonth': '10',
 'acquisitionDay': '31',
 'processingYear': '2020',
 'processingMonth': '11',
 'processingDay': '06',
 'collectionNumber': '02',
 'collectionCategory': 'T2',
 'scene': 'LC08_L2SP_001062_20201031_20201106_02_T2',
 'date': '2020-10-31',
 '_processingLevelNum': '2',
 'category': 'standard',
 'sensor_name': 'oli-tirs',
 '_sensor_s3_prefix': 'oli-tirs',
 'bands': ('QA_PIXEL',
  'QA_RADSAT',
  'SR_B1',
  'SR_B2',
  'SR_B3',
  'SR_B4',
  'SR_B5',
  'SR_B6',
  'SR_B7',
  'SR_QA_AEROSOL',
  'ST_ATRAN',
  'ST_B10',
  'ST_CDIST',
  'ST_DRAD',
  'ST_EMIS',
  'ST_EMSD',
  'ST_QA',
  'ST_TRAD',
  'ST_URAD')}

with LandsatC2Reader("LC08_L2SP_001062_20201031_20201106_02_T2") as landsat:
    print(landsat._get_band_url("SR_B2"))

> s3://usgs-landsat/collection02/level-2/standard/oli-tirs/2020/001/062/LC08_L2SP_001062_20201031_20201106_02_T2/LC08_L2SP_001062_20201031_20201106_02_T2_SR_B2.TIF

Each dataset has a specific scene id format:

!!! note "Scene ID formats"

- Landsat
    - link: [rio_tiler_pds.landsat.utils.sceneid_parser](https://github.com/cogeotiff/rio-tiler-pds/blob/e4421d3cf7c23b7b3552b8bb16ee5913a5483caf/rio_tiler_pds/landsat/utils.py#L35-L56)
    - regex: `^L[COTEM]0[0-9]_L\d{1}[A-Z]{2}_\d{6}_\d{8}_\d{8}_\d{2}_(T1|T2|RT)$`
    - example: `LC08_L1TP_016037_20170813_20170814_01_RT`

- Sentinel 1 L1C
    - link: [rio_tiler_pds.sentinel.utils.s1_sceneid_parser](https://github.com/cogeotiff/rio-tiler-pds/blob/e4421d3cf7c23b7b3552b8bb16ee5913a5483caf/rio_tiler_pds/sentinel/utils.py#L98-L121)
    - regex: `^S1[AB]_(IW|EW)_[A-Z]{3}[FHM]_[0-9][SA][A-Z]{2}_[0-9]{8}T[0-9]{6}_[0-9]{8}T[0-9]{6}_[0-9A-Z]{6}_[0-9A-Z]{6}_[0-9A-Z]{4}$`
    - example: `S1A_IW_GRDH_1SDV_20180716T004042_20180716T004107_022812_02792A_FD5B`

- Sentinel 2 JPEG2000 and Sentinel 2 COG
    - link: [rio_tiler_pds.sentinel.utils.s2_sceneid_parser](https://github.com/cogeotiff/rio-tiler-pds/blob/e4421d3cf7c23b7b3552b8bb16ee5913a5483caf/rio_tiler_pds/sentinel/utils.py#L25-L60)
    - regex: `^S2[AB]_[0-9]{2}[A-Z]{3}_[0-9]{8}_[0-9]_L[0-2][A-C]$` or `^S2[AB]_L[0-2][A-C]_[0-9]{8}_[0-9]{2}[A-Z]{3}_[0-9]$`
    - example: `S2A_29RKH_20200219_0_L2A`, `S2A_L1C_20170729_19UDP_0`, `S2A_L2A_20170729_19UDP_0`

- CBERS
    - link: [rio_tiler_pds.cbers.utils.sceneid_parser](https://github.com/cogeotiff/rio-tiler-pds/blob/e4421d3cf7c23b7b3552b8bb16ee5913a5483caf/rio_tiler_pds/cbers/utils.py#L28-L43)
    - regex: `^CBERS_(4|4A)_\w+_[0-9]{8}_[0-9]{3}_[0-9]{3}_L\w+$`
    - example: `CBERS_4_MUX_20171121_057_094_L2`, `CBERS_4_AWFI_20170420_146_129_L2`, `CBERS_4_PAN10M_20170427_161_109_L4`, `CBERS_4_PAN5M_20170425_153_114_L4`, `CBERS_4A_WPM_20200730_209_139_L4`

- MODIS (PDS and Astraea)
    - link: [rio_tiler_pds.modis.utils.sceneid_parser](https://github.com/cogeotiff/rio-tiler-pds/blob/c533d38330f46738c46cb9927dbe91b299dc643d/rio_tiler_pds/modis/utils.py#L29-L42)
    - regex: `^M[COY]D[0-9]{2}[A-Z0-9]{2}\.A[0-9]{4}[0-9]{3}\.h[0-9]{2}v[0-9]{2}\.[0-9]{3}\.[0-9]{13}$`
    - example: `MCD43A4.A2017006.h21v11.006.2017018074804`

Band Per Asset/File

rio-tiler-pds Readers assume that bands (e.g eo:band in STAC) are stored in separate files.

$ aws s3 ls s3://usgs-landsat/collection02/level-2/standard/oli-tirs/2020/001/062/LC08_L2SP_001062_20201031_20201106_02_T2/ --request-payer
LC08_L2SP_001062_20201031_20201106_02_T2_ANG.txt
LC08_L2SP_001062_20201031_20201106_02_T2_MTL.json
LC08_L2SP_001062_20201031_20201106_02_T2_MTL.txt
LC08_L2SP_001062_20201031_20201106_02_T2_MTL.xml
LC08_L2SP_001062_20201031_20201106_02_T2_QA_PIXEL.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_QA_RADSAT.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_B1.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_B2.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_B3.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_B4.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_B5.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_B6.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_B7.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_QA_AEROSOL.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_SR_stac.json
LC08_L2SP_001062_20201031_20201106_02_T2_ST_ATRAN.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_B10.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_CDIST.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_DRAD.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_EMIS.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_EMSD.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_QA.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_TRAD.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_URAD.TIF
LC08_L2SP_001062_20201031_20201106_02_T2_ST_stac.json
LC08_L2SP_001062_20201031_20201106_02_T2_thumb_large.jpeg
LC08_L2SP_001062_20201031_20201106_02_T2_thumb_small.jpeg

When reading data or metadata, readers will merge them.

e.g

with S2COGReader("S2A_L2A_20170729_19UDP_0") as sentinel:
    img = sentinel.tile(78, 89, 8, bands=("B01", "B02"))
    assert img.data.shape == (2, 256, 256)

    stats = sentinel.statistics(bands=("B01", "B02"))
    print(stats)
    >> {
      'B01': BandStatistics(
        min=2.0,
        max=17132.0,
        mean=2183.7570706659685,
        count=651247.0,
        sum=1422165241.0,
        std=3474.123975478363,
        median=370.0,
        majority=238.0,
        minority=2.0,
        unique=15112.0,
        histogram=[
          [476342.0, 35760.0, 27525.0, 24852.0, 24379.0, 23792.0, 20891.0, 13602.0, 3891.0, 213.0],
          [2.0, 1715.0, 3428.0, 5141.0, 6854.0, 8567.0, 10280.0, 11993.0, 13706.0, 15419.0, 17132.0]
        ],
        valid_percent=62.11,
        masked_pixels=397329.0,
        valid_pixels=651247.0,
        percentile_2=179.0,
        percentile_98=12465.0
      ),
      'B02': BandStatistics(
        min=1.0,
        max=15749.0,
        mean=1941.2052554560712,
        count=651247.0,
        sum=1264204099.0,
        std=3130.545395156859,
        median=329.0,
        majority=206.0,
        minority=11946.0,
        unique=13904.0,
        histogram=[
          [479174.0, 34919.0, 27649.0, 25126.0, 24913.0, 24119.0, 20223.0, 12097.0, 2872.0, 155.0],
          [1.0, 1575.8, 3150.6, 4725.4, 6300.2, 7875.0, 9449.8, 11024.6, 12599.4, 14174.199999999999, 15749.0]
        ],
        valid_percent=62.11,
        masked_pixels=397329.0,
        valid_pixels=651247.0,
        percentile_2=134.0,
        percentile_98=11227.079999999958
      )}

      print(stats["B01"].min)
      >> 2.0

Mosaic Reader: Copernicus DEM

The Copernicus DEM GLO-30 and GLO-90 readers are not per scene but mosaic readers. This is possible because the dataset is a global dataset with file names having the geo-location of the COG, meaning we can easily contruct a filepath from a coordinate.

from rio_tiler_pds.copernicus.aws import Dem30Reader

with Dem30Reader() as dem:
    print(dem.assets_for_point(-57.2, -11.2))

>> ['s3://copernicus-dem-30m/Copernicus_DSM_COG_10_S12_00_W058_00_DEM/Copernicus_DSM_COG_10_S12_00_W058_00_DEM.tif']

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.