Sentinel-2 and Sentinel-2 Theia
It also implements additional sensor-agnostic features:
load: Load many band types:
stack: Stack all these type of bands
The main features of EOReader are gathered hereunder. For optical data:
>>> from eoreader.reader import Reader >>> from eoreader.bands.alias import * >>> # Landsat-5 MSS path >>> l5_path = "LM05_L1TP_200029_19841014_20200902_02_T2.tar" >>> # Create the reader object and open satellite data >>> eoreader = Reader() >>> # The Reader will recognize the satellite type from its structure >>> l5_prod = eoreader.open(l5_path) >>> # Specify a DEM to load HILLSHADE AND SLOPE bands >>> import os >>> from eoreader.env_vars import DEM_PATH >>> os.environ[DEM_PATH] = "my_dem.tif" >>> # Load some bands and index: they will all share the same metadata >>> bands = l5_prod.load([NDVI, GREEN, HILLSHADE, CLOUDS]) >>> # Create a stack with some other bands >>> stack = l5_prod.stack([NDWI, RED, SLOPE]) >>> # Read Metadata >>> mtd, namespace = l5_prod.read_mtd()
For SAR data:
>>> from eoreader.reader import Reader >>> from eoreader.bands.alias import * >>> # 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 >>> import os >>> from eoreader.env_vars import DEM_PATH >>> 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
Available notebooks provided as examples:
You can install EOReader via pip:
pip install eoreader
EOReader mainly relies on
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.
You can install EOReader via conda:
conda config --env --set channel_priority strict
conda install -c conda-forge eoreader
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 !
EOReader is licensed under Apache License v2.0. See LICENSE file for details.
- Main features
- Optical data
- SAR data
- Implemented SAR satellites
- SAR Bands
- DEM bands
- Default resolution
- GPT graphs
- Documentary Sources
- Frequently Asked Questions (FAQ)