The UKIS-pysat package provides generic classes and functions to query, access and process multi-spectral and SAR satellite images.
Download satellites data from different sources (currently Earth Explorer, SciHub, STAC), deal with and structure metadata.
Work with you local satellite data files and read information out of file names and metadata files. Currently, focusing on Sentinel-1.
Reading satellite data and performing simple, but cumbersome tasks. This is just a layer on top of rasterio for stuff we often need. It can very well be that using rasterio directly is often the better choice.
Read the documentation for more details: https://ukis-pysat.readthedocs.io.
Here's an example about some basic features, it might also help to read through the tests. ###Sentinel Dataset
# import all the required libraries
from ukis_pysat.data import Source
from ukis_pysat.file import get_sentinel_scene_from_dir
from ukis_pysat.members import Datahub, Platform
from ukis_pysat.raster import Image
# connect to Copernicus Open Access Hub and query metadata
src = Source(Datahub.Scihub)
meta = src.query_metadata(
platform=Platform.Sentinel2,
date=("20200101", "NOW"),
aoi=(11.90, 51.46, 11.94, 51.50),
cloud_cover=(0, 50),
)
for item in meta: # item is now a PySTAC item
print(item.id)
uuid = item.properties["srcuuid"]
# download geocoded quicklook and image
src.download_quicklook(product_uuid=uuid, target_dir="/users/username/tmp")
src.download_image(product_uuid=uuid, target_dir="/users/username/tmp")
break
# get sentinel scene from directory
with get_sentinel_scene_from_dir("/users/username/tmp") as (full_path, ident):
with Image(full_path.join("pre_nrcs.tif")) as img:
# scale the image array, having one band
img.arr = img.arr * 0.3For working with the Landsat we need an item id for downloading the product Check Pystac documentation for more functionality on STAC.
To use ukis_pysat.data and to download from the respective Datahub you need to set the credentials as environment variables.
For EarthExplorer that's:
EARTHEXPLORER_USER=your_username
EARTHEXPLORER_PW=your_password
For SciHub that's:
SCIHUB_USER=your_username
SCIHUB_PW=your_password
The easiest way to install pysat is through pip. Be aware, that Rasterio requires GDAL >= 1.11, < 3.1.
Most users will want to do this:
pip install ukis-pysat[complete] # install everythingThere's also some lighter versions with less dependencies:
pip install ukis-pysat # only install core dependencies (ukis_pysat.file can be used)
pip install ukis-pysat[data] # also install dependencies for ukis_pysat.data
pip install ukis-pysat[raster] # also install dependencies for ukis_pysat.rasterSome helper functions might need additional dependencies like pandas, dask[array] or utm. If this is the case you will receive an ImportError.
For the latest list of dependencies check the requirements.
The UKIS team creates and adapts libraries which simplify the usage of satellite data. Our team includes (in alphabetical order):
- Boehnke, Christian
- Fichtner, Florian
- Mandery, Nico
- Martinis, Sandro
- Riedlinger, Torsten
- Wieland, Marc
German Aerospace Center (DLR)
This software is licensed under the Apache 2.0 License.
Copyright (c) 2020 German Aerospace Center (DLR) * German Remote Sensing Data Center * Department: Geo-Risks and Civil Security
See changelog.
The UKIS team welcomes contributions from the community. For more detailed information, see our guide on contributing if you're interested in getting involved.
The DLR project Environmental and Crisis Information System (the German abbreviation is UKIS, standing for Umwelt- und Kriseninformationssysteme aims at harmonizing the development of information systems at the German Remote Sensing Data Center (DFD) and setting up a framework of modularized and generalized software components.
UKIS is intended to ease and standardize the process of setting up specific information systems and thus bridging the gap from EO product generation and information fusion to the delivery of products and information to end users.
Furthermore, the intention is to save and broaden know-how that was and is invested and earned in the development of information systems and components in several ongoing and future DFD projects.