Tag: qgis

Sorry, this entry is only available in French.

Oslandia is the main partner of OPENGIS.ch around QField. We are proud today to forward the announcement of the new QField release 3.4 “Ebo”.

Main highlights

A new geofencing framework has landed, enabling users to configure QField behaviors in relation to geofenced areas and user positioning. Geofenced areas are defined at the project-level and shaped by polygons from a chosen vector layer. The three available geofencing behaviours in this new release are:

• Alert user when inside an area polygon;
• Alert user when outside all defined area polygons and
• Inform the user when entering and leaving an area polygons.

In addition to being alerted or informed, users can also prevent digitizing of features when being alerted by the first or second behaviour. The configuration of this functionality is done in QGIS using QFieldSync.

Pro tip: geofencing settings are embedded within projects, which means it is easy to deploy these constraints to a team of field workers through QFieldCloud. Thanks Terrex Seismic for sponsoring this functionality.

QField now offers users access to a brand new processing toolbox containing over a dozen algorithms for manipulating digitized geometries directly in the field. As with many parts of QField, this feature relies on QGIS’ core library, namely its processing framework and the numerous, well-maintained algorithms it comes with.

The algorithms exposed in QField unlock many useful functionalities for refining geometries, including orthogonalization, smoothing, buffering, rotation, affine transformation, etc. As users configure algorithms’ parameters, a grey preview of the output will be visible as an overlay on top of the map canvas.

To reach the processing toolbox in QField, select one or more features by long-pressing on them in the features list, open the 3-dot menu and click on the process selected feature(s) action. Are you excited about this one? Send your thanks to the National Land Survey of Finland, who’s support made this a reality.

QField’s camera has gained support for customized ratio and resolution of photos, as well as the ability to stamp details – date and time as well as location details – onto captured photos. In fact, QField’s own camera has received so much attention in the last few releases that it was decided to make it the default one. On supported platforms, users can switch to their OS camera by disabling the native camera option found at the bottom of the QField settings’ general tab.

Wait, there’s more

There are plenty more improvements packed into this release from project variables editing using a revamped variables editor through to integration of QField documentation help in the search bar and the ability to search cloud project lists. Read the full 3.4 changelog to know more, and enjoy the release!

Contact us !

A question concerning QField ? Interested in QField deployment ? Do not hesitate to contact Oslandia to discuss your project !

After the initial ChatGPT hype in 2023 (when we saw the first LLM-backed QGIS plugins, e.g. QChatGPT and QGPT Agent), there has been a notable slump in new development. As far as I can tell, none of the early plugins are actively maintained anymore. They were nice tech demos but with limited utility.

However, in the last month, I saw two new approaches for combining LLMs with QGIS that I want to share in this post:

IntelliGeo plugin: generating PyQGIS scripts or graphical models

At the QGIS User Conference in Bratislava, I had the pleasure to attend the “Large Language Models and GIS” workshop presented by Gustavo Garcia and Zehao Lu from the the University of Twente. There, they presented the IntelliGeo Plugin which enables the automatic generation of PyQGIS scripts and graphical models.

The workshop was packed. After we installed all dependencies and the plugin, it was exciting to test the graphical model generation capabilities. During the workshop, we used OpenAI’s API but the readme also mentions support for Cohere.

I was surprised to learn that even simple graphical models are actually pretty large files. This makes it very challenging to generate and/or modify models because they take up a big part of the LLM’s context window. Therefore, I expect that the PyQGIS script generation will be easier to achieve. But, of course, model generation would be even more impressive and useful since models are easier to edit for most users than code.

ChatGeoAI: chat with PyQGIS

ChatGeoAI is an approach presented in Mansourian, A.; Oucheikh, R. (2024). ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models. ISPRS Int. J. Geo-Inf., 13, 348.

It uses a fine-tuned Llama 2 model in combination with spaCy for entity recognition and WorldKG ontology to write PyQGIS code that can perform a variety of different geospatial analysis tasks on OpenStreetMap data.

The paper is very interesting, describing the LLM fine-tuning, integration with QGIS, and evaluation of the generated code using different metrics. However, as far as I can tell, the tool is not publicly available and, therefore, cannot be tested.

Are you aware of more examples that integrate QGIS with LLMs? Please share them in the comments below. I’d love to hear about them.

Today marks the release of Trajectools 2.3 which brings a new set of algorithms, including trajectory generalizing, cleaning, and smoothing.

To give you a quick impression of what some of these algorithms would be useful for, this post introduces a trajectory preprocessing workflow that is quite general-purpose and can be adapted to many different datasets.

We start out with the Geolife sample dataset which you can find in the Trajectools plugin directory’s sample_data subdirectory. This small dataset includes 5908 points forming 5 trajectories, based on the trajectory_id field:

We first split our trajectories by observation gaps to ensure that there are no large gaps in our trajectories. Let’s make at cut at 15 minutes:

This splits the original 5 trajectories into 11 trajectories:

When we zoom, for example, to the two trajectories in the north western corner, we can see that the trajectories are pretty noisy and there’s even a spike / outlier at the western end:

If we label the points with the corresponding speeds, we can see how unrealistic they are: over 300 km/h!

Let’s remove outliers over 50 km/h:

Better but not perfect:

Let’s smooth the trajectories to get rid of more of the jittering.

(You’ll need to pip/mamba install the optional stonesoup library to get access to this algorithm.)

Depending on the noise values we chose, we get more or less smoothing:

Let’s zoom out to see the whole trajectory again:

Feel free to pan around and check how our preprocessing affected the other trajectories, for example:

Sorry, this entry is only available in French.

Sorry, this entry is only available in French.

Sorry, this entry is only available in French.

Sorry, this entry is only available in French.

If you downloaded Trajectools 2.1 and ran into troubles due to the introduced scikit-mobility and gtfs_functions dependencies, please update to Trajectools 2.2.

This new version makes it easier to set up Trajectools since MovingPandas is pip-installable on most systems nowadays and scikit-mobility and gtfs_functions are now truly optional dependencies. If you don’t install them, you simply will not see the extra algorithms they add:

If you encounter any other issues with Trajectools or have questions regarding its usage, please let me know in the Trajectools Discussions on Github.

Swiss elevation profiles

Get high-precision elevation profiles in QGIS right from Swisstopo’s official profile service, based on swissALTI3D data!

Thanks to this integration, you can take advantage of existing QGIS features, such as exporting 2d/3d features or distance/elevation tables, as well as displaying profiles directly in QGIS layouts.

Tip: Swiss elevation profiles will be available as long as the Swiss Locator plugin is installed and active. Should you need to turn Swiss elevation profiles off to create other profiles with your own data, go to the Plugin manager and deactivate the plugin in the meantime.

For developers

We’re paving the way for adding custom elevation profiles to QGIS. For that, we’ve added a QGIS profile source registry so that plugin developers can register their own profile sources (e.g., based on profile web services, just like we did here) and make them available for QGIS end users. The registry is available from QGIS 3.38. It’s your turn!

Thanks to the QGIS user group Switzerland for funding this feature!

Swiss vector tiles base maps

Loading Swiss vector tiles is now easier than ever. Just go to the locator bar, type the prefix “chb” (add a white space after that) and you’ll get a list of available and already styled Swiss vector tiles layers. Some of them will even load grouped auxiliary imagery for reference.

Vector tiles will be loaded at the bottom of the QGIS layer tree as base maps, so you will see all your data on top of them.

Vector tiles are optimized for local caching and scale-independent rendering. This also makes it a perfect fit for adding it to your QField project.

There are a couple of different vector tile sets available:

leichte-basiskarte

Light base map

Similar to the leichte-basiskarte layer, but using an older version of the data source and adjusted styles.

leichte-basiskarte-imagery (with WMTS sublayer)

Imagery base map (with WMTS sublayer)

This layer is similar to the leichte-basiskarte-imagery layer, but it uses an older version of the data source and adjusted styles.

Base map

See the official services documentation for details on data sources and styles.

Fixes

Thanks to your feedback, we’ve also fixed some issues. Don’t hesitate to reach out to us at GitHub if you’d like to suggest or report something related to the Swiss Locator plugin.

Happy (and now more powerful) mapping!