eoreader 0.11.2

Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and index in a sensor-agnostic way.

EOReader

EOReader is a remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and index in a sensor-agnostic way.

Optical sensors	SAR sensors
Sentinel-2 and Sentinel-2 Theia Sentinel-3 OLCI and Sentinel-3 SLSTR Landsat 1 to 8 (MSS, TM, ETM and OLCI) PlanetScope Pleiades SPOT 6-7 WorldView-2 to 4, GeoEye-1 (and other Maxar sensors)	Sentinel-1 COSMO-Skymed 1st and 2nd Generation TerraSAR-X, TanDEM-X and PAZ RADARSAT-2 RADARSAT-Constellation ICEYE

It also implements additional sensor-agnostic features:

• load: Load many band types:
  • satellite bands (optical or SAR)
  • index
  • cloud bands
  • DEM bands
• stack: Stack all these type of bands

EOReader works with xarrays.DataArray and geopandas.GeoDataFrames

Python Quickstart

The main features of EOReader are gathered hereunder. For optical data:

>>> import os
>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> from eoreader.env_vars import DEM_PATH

>>> # Sentinel-2 path
>>> s2_path = "S2B_MSIL1C_20181126T022319_N0207_R103_T51PWM_20181126T050025.SAFE"

>>> # Create the reader object and open satellite data
>>> eoreader = Reader()

>>>  # The Reader will recognize the satellite type from its structure
>>> s2_prod = eoreader.open(s2_path)

>>> # Specify a DEM to load HILLSHADE AND SLOPE bands
>>> os.environ[DEM_PATH] = "my_dem.tif"

>>> # Load some bands and index: they will all share the same metadata
>>> bands = s2_prod.load([NDVI, GREEN, HILLSHADE, CLOUDS])

>>> # Create a stack with some other bands
>>> stack = s2_prod.stack([NDWI, RED, SLOPE])

>>> # Read Metadata
>>> mtd, namespace = s2_prod.read_mtd()

For SAR data:

>>> import os
>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> from eoreader.env_vars import DEM_PATH

>>> # Sentinel-1 GRD path
>>> s1_path = "S1B_EW_GRDM_1SDH_20200422T080459_20200422T080559_021254_028559_784D.zip"

>>> # Create the reader object and open satellite data
>>> eoreader = Reader()

>>> # The Reader will recognize the satellite type from its structure
>>> s1_prod = eoreader.open(s1_path)

>>>  # Specify a DEM to load DEM and SLOPE bands
>>> os.environ[DEM_PATH] = "my_dem.tif"

>>> # Load some bands and index: they will all share the same metadata
>>> bands = s1_prod.load([VV, VV_DSPK, DEM])

>>> # Create a stack with some other bands
>>> stack = s1_prod.stack([VV, VV_DSPK, SLOPE])

>>> # Read Metadata
>>> mtd, namespace = s1_prod.read_mtd()

SAR products need SNAP gpt to be orthorectified and calibrated. Ensure that you have the folder containing your gpt executable in your PATH.

Documentation

The API documentation can be found here.

Examples

Available notebooks provided as examples:

• Basic tutorial
• SAR data
• VHR data
• Sentinel-3 data
• Water detection on multiple products
• Custom stacks
• Methods to clean optical bands
• S3 Compatible Storage
• Dask

Installation

Pip

You can install EOReader via pip:

pip install eoreader

EOReader mainly relies on geopandas and rasterio (through rioxarray).

On Windows and with pip, you may face installation issues due to GDAL. The well known workaround of installing from Gohlke's wheels also applies here. Please look at the rasterio page to learn more about that.

Conda

Command line

You can install EOReader via conda:

conda config --env --set channel_priority strict

conda install -c conda-forge eoreader

But for the moment, the lib used for caching objects (methodtools) is not available on conda. So please install it via pip (pip install methodtools) before using EOReader !

Configuration file (preferred method)

You can use a configuration file like this (environment.yml) for conda to create your environment:

name: eoreader
channels:
  - conda-forge
dependencies:         # everything under this, installed by conda
  - python=3.7
  - eoreader
  - pip
  - pip:                # everything under this, installed by pip
      - methodtools

And create your environment like that: conda env create -f environment.yml.

Context

SERTIT is part of the Copernicus Emergency Management Service rapid mapping and risk and recovery teams.

In these activations, we need to deliver information (such as flood or fire delineations, landslides mapping, etc.) based on various sensors (more than 10 optical and 5 SAR). As every minute counts in production, it seemed crucial to harmonize the ground on which are built our production tools, in order to make them as sensor-agnostic as possible.

Thus, thanks to EOReader, these tools are made independent to the sensor:

• the algorithm (and its developer) can focus on its core tasks (such as extraction) without taking into account the sensor characteristics (how to load a band, which band correspond to which band number, which band to use for this index...)
• the addition of a new sensor is done effortlessly (if existing in EOReader) and without any modification of the algorithm
• the maintenance is simplified and the code is way more readable (no more ifs regarding the sensor type!)
• the testing is also simplified as the sensor-related parts are tested in this library

However, keep in mind that the support of all the sensors used in CEMS is done in a best effort mode, especially for commercial data. Indeed, we may not have faced every product type, sensor mode or order configuration, so some details may be missing. If this happens to you, do not hesitate to make a PR or write an issue about that !

License

EOReader is licensed under Apache License v2.0. See LICENSE file for details.

Authors

EOReader has been created by ICube-SERTIT.

Credits

EOReader is built on top of amazing libs, without which it couldn't have been coded:

• geopandas
• rasterio
• xarray
• rioxarray