Tag: gis

We have recently been working for the French Space Agency ( CNES ) who needed to store and visualize satellite rasters in a cloud platform. They want to access the image raw data, with no transformation, in order to fullfill deep analysis like instrument calibration. Using classic cartographic server standard like WMS or TMS is not an option because those services transform datasets in already rendered tiles.

We chose to use a quite recent format managed by GDAL, the COG (Cloud Optimize Geotiff) and target OVH cloud platform for it provides OpenStack, a open source cloud computing platform.

How it works

A COG file is a GEOTiff file which inner structure is tiled, meaning that the whole picture is divided in fixed size tile (256 x 256 pixels for instance) so you can efficiently retrieve parts of the raster. In addition to the HTTP/1.1 standard feature range request, it is possible to get specific tiles of an image through the network without downloading the entire raster.

We used a service provided by OpenStack, called Object Storage to serve the COG imagery. Object storage allows to store and retrieve file as objects using HTTP GET/POST requests.

Why not WCS ?

Web Coverage Service standard could have been an option. A WCS server can serve raw data according to a given geographic extent. It’s completely possible to deploy a container or a VPS (Virtual Private Server) running a WCS Server in a cloud plateform. The main advantages of the COG solution over WCS Server is that you don’t have to deal with the burden of deploying a server, like giving it ressources, configuring load balancing, handle updates, etc…

The beauty of COG solution is its simplicity. It is only HTTP requests, and everything else (rendering for instance) is done on the client side.

Step by step

Here are the different steps you’d have to go through if you’re willing to navigate in a big raster image directly from the cloud.

First, let’s generate a COG file

gdal_translate inputfile.tif cogfile.tif -co TILED=YES -co COPY_SRC_OVERVIEWS=YES -co COMPRESS=DEFLATE

Install your openstack-client, it can be achieved easily with Python pip install command line

$ pip install python-openstackclient

Next, configure your openstack client in order to generate an athentification token. To do so you need to download your project specific openrc file to setup your environment)

$ source myproject-openrc.sh
Please enter your OpenStack Password for project myproject as user myuser:
**********
$ openstack token issue                                 
+------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Field      | Value                                                                                                                                                                                   |
+------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| expires    | 2020-07-21T08:15:12+0000                                                                                                                                                                |
| id         | xxxx_my_token_xxxx
| project_id | 97e2e750f1904b41b76f80a50dabde0a                                                                                                                                                        |
| user_id    | 18f7ccaf1a2d4344a4e35f0d84eb065e                                                                                                                                                        |
+------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

You are now good to push you COG file to the cloud instance

openstack object create MyContainer cogfile.tif --name cogfile.tif

Before starting QGIS, 2 environment variables need to be set.  (replace xxxx_my_token_xxxx with the token you’d just come to generate)

$ export SWIFT_AUTH_TOKEN=xxxx_my_token_xxxx
$ export SWIFT_STORAGE_URL=https://storage.sbg.cloud.ovh.net/v1/AUTH_$OS_PROJECT_ID

It can also be done directly from the QGIS Python console by setting those variable using the os.environ.

Finally, add a cloud raster data source in in QGIS

You can now navigating into your image directly reading it from the cloud

© CNES 2018, Distribution Airbus DS

Performances

While panning in the map, QGIS will download only few tiles from the image in order to cover the view extent. The display latency that you could see in the video depends essentially on:

• The number of band of your image
• The pixel size
• Your internet connection (mine, the one use for the video, is not an awesome one)

Note that the white flickering that you could see when you move in the map and the raster is refreshed should be removed in next version of QGIS according to this QEP.

What’s next ?

Thanks so much to the GDAL and QGIS contributors for adding such a nice feature ! It brings lots of possibilities for organizations that have to deal with great number of big raster and just want to explore part of it.

We are already thinking about further improvments (ease authentification, better integration with processing…), so if you’re willing to fund them or just want to know more about QGIS, feel free to contact us at infos+data@oslandia.com. And please have a look at our support offering for QGIS.

Why python data providers?

My main reasons for having Python data provider were:

• quick prototyping
• web services
• why not?

This topic has been floating in my head for a while since I decided to give it a second look and I finally implemented it and merged for the next 3.2 release.

How it’s been done

To make this possible I had to:

• create a public API for registering the providers
• create the Python bindings (the hard part)
• create a sample Python vector data provider (the boring part)
• make all the tests pass

First, let me say that it wasn’t like a walk in the park: the Python bindings part is always like diving into woodoo and black magic recipes before I can get it to work properly.

For the Python provider sample implementation I decided to re-implement the memory (aka: scratch layers) provider because that’s one of the simplest providers and it does not depend on any external storage or backend.

How to and examples

For now, the main source of information is the API and the tests:

• • https://github.com/qgis/QGIS/blob/master/src/core/qgsproviderregistry.h#L188
  • https://github.com/qgis/QGIS/blob/master/tests/src/python/provider_python.py
  • https://github.com/qgis/QGIS/blob/master/tests/src/python/test_provider_python.py

To register your own provider (PyProvider in the snippet below) these are the basic steps:

metadata = QgsProviderMetadata(PyProvider.providerKey(), PyProvider.description(), PyProvider.createProvider)
QgsProviderRegistry.instance().registerProvider(metadata)

To create your own provider you will need at least the following components:

• the provider class itself (subclass of QgsVectorDataProvider)
• a feature source (subclass of QgsAbstractFeatureSource)
• a feature iterator (subclass of QgsAbstractFeatureIterator)

Be aware that the implementation of a data provider is not easy and you will need to write a lot of code, but at least you could get some inspiration from the existing example.

Enjoy wirting data providers in Python and please let me know if you’ve fond this implementation useful!

• the future of processing providers: should they be part of QGIS code or handled independently as plugins?
• the road forward to a better bug reporting system and CI platform: move to gitlab?
• the certification program for QGIS training courses: how (and how much) training companies should give back to the project?
• SWOT analysis of current QGIS project: very interesting discussion about the status of the project.
• QGIS Qt Quick modules for mobile QGIS app

• How to set up a development environment and make your first pull request
• How to write tests for QGIS (in both python and C++)

./configure -prefix /opt/qt593 -debug -opensource -confirm-license -ccache -platform linux-clang

make -j9
sudo make install

/opt/qt593/bin/qmake WebKit.pro
make -j9
sudo make install

/opt/qt593/bin/qmake qscintilla.pro
make -j9
sudo make install

/opt/qt593/bin/qmake qwt.pro
make -j9
sudo make install

• fixed some bugs and feature requests on the official QGIS plugin repo that I’m maintaining since the very beginning
• make the QGIS official plugin repository website mobile-friendly
• QGIS Server Python Plugin API refactoring, I’ve completed the work on the new API, thanks to the ongoing server refactoring it’s now much cleaner than it was in the first version
• attribute table bugs: I started to address some nasty bugs in the attribute table, some of those were fixed during the week right after the hackfest
• unified add layer button, we had a productive meeting where we decided the path forward to implement this feature, thanks to Boundless that is funding the development, this feature is what’s I’m currently working on these days