The QGISUC2025 team has done an awesome job recording and editing the conference presentations. All “presentation” type talks where the presenter has accepted to be published are now available in a dedicated list on the QGIS Youtube channel.

I also had the pleasure of presenting our Trajectools plugin and you can see this talk here:

Thank you to all the organizers, speakers, and participants for the great time!

It was such a pleasure to be part of the QGIS User Conference 2025 in Norrköping! The event was extremely well organised — a big thank you to the amazing local team for pulling it all together so smoothly. Personally, it felt special to be back in Sweden, almost 20 years after my Uppsala university days. I truly enjoyed giving the opening keynote and sharing the latest from the QGIS project — and of course, showcasing all the QField greatness we’ve been working so hard on

Talks & Presentations

QGIS.org updates

As Chair of the QGIS.org association, I had the opportunity to share recent updates from the QGIS community. I spoke about ongoing development efforts, community growth, funding initiatives, and collaborations that help keep the project moving forward.
The focus was on the people who make QGIS possible — contributors, sponsors, local user groups, and everyday users — and how their involvement continues to shape the project’s direction and ensure its long-term sustainability.

Slides here (unfortunately keynotes and workshops were not recorded)

Extending QFieldCloud – Ideas and Practical Examples

In this talk, Michael, one of our Full stack GeoNinja and Web Cartography teacher, explained how QFieldCloud can be extended by integrating additional Django apps. This allowed, for example, the generation of QField projects, reacting to events from fieldwork, adding new websites and APIs, and executing entire QGIS models as QFieldCloud jobs.

After a technical introduction, various practical examples were explored. It was shown how OpenStreetMap data can be fully automated to download offline-capable QField projects. Attendees got inspired by how an own WebGIS is brought to life in QFieldCloud using OpenLayers. Furthermore, he demonstrated how remote sensing data can be downloaded, analysed in a QGIS pipeline, and the results made available in QField projects. Finally, the discussion focused on how these capabilities can be optimally used in combination with QField plugins.

SIGNALO: An Open-Source Solution for Mapping Road Signs in QGIS 

Presented by Denis, our Industry Solution Team Lead, SIGNALO is a QGIS-based solution for mapping road signs, powered by a PostGIS database. It addresses the challenge of representing vertical data on maps while ensuring compliance with Swiss norms, yet remains highly customizable for use at local, regional, or national levels. Moreover, the flexible design allows for easy adaptation to other countries.

In this talk, Denis explored both the technical foundations of the project and the organizational strategies that enable its open-source development.

1.5 Million Reasons to Use QField

In this talk, I shared our vision for the future of QField — the world’s most popular open-source mobile GIS solution. With over 1.4 million downloads and 500,000 active users, QField is making a real difference for fieldwork around the globe.
I spoke about where we’re headed next, what new features are coming, and how we at OPENGIS.ch are working to empower professionals across all sectors with powerful, flexible, and open tools for mobile geospatial workflows.

Standardizing Groundwater Data Collection with QField

We were excited to see Alexandra Nozik from the Leibniz Centre for Tropical Marine Research (ZMT) present her work on a QField project designed to standardize groundwater data collection in remote tropical regions. The setup uses QGIS layers, predefined parameters, and metadata standards to ensure high-quality, consistent field data. Integrated with QFieldCloud, the workflow improves data accuracy, reduces data loss, and enables real-time collaboration. The project will be published on GitHub as a ready-to-use package, supporting reliable and comparable groundwater data collection across the scientific community.

QField and QFieldCloud – seamless fieldwork for QGIS 

In this workshop, Zsanett, QField Product Manager, went through the complete fieldwork process: setting up a QGIS project, publishing the project via QFieldCloud, collecting data via the QField mobile app and synchronising the field data back to your main dataset in the office. QField works on top of QGIS and allows users to set up maps and forms in QGIS on their workstation and deploy them in the field. QField uses QGIS’s data providers (OGR, GDAL, PostGIS and others) and supports most common file formats. QField combines a minimal design with sophisticated technology that allows intuitive viewing and editing of data. QField’s map rendering is supported by the QGIS rendering engine, so the results are identical and the full range of styling options available on the desktop is available. Editing forms in QField respect the QGIS configuration and are optimised for touch interaction. QFieldCloud makes field collaboration much easier. Participants learned about configuring users with different rights, collecting offline and online data, and synchronizing field data and QGIS project data.

Our first international QField Day

On June 4th, the first international QField Day took place in Norrköping, right after the QGIS User Conference. This free half-day event was dedicated to QField, QFieldCloud, and the mobile GIS community, bringing together users, contributors, and developers for an afternoon of field-tested workflows, live demos, community stories, and open discussions. It was a great opportunity to connect, exchange ideas, and explore the future of mobile geospatial tools in the open-source ecosystem.
Definitely not our last one.

Supporting Open Source

We were proud to support QGIS UC25 in Norrköping, Sweden, as Platinum Sponsors — reaffirming our commitment to the open-source geospatial community and the continued growth of the QGIS ecosystem.

Looking Ahead

We’re already looking forward to the next gathering — QGIS UC26 will take place in Switzerland !

After the conference, I joined the contributor meeting along with four QGIS developers from OPENGIS.ch. It was a fantastic chance to collaborate in person, help shape the future of QGIS, and reconnect with old friends from the community.

Building on top of the last release which introduced background tracking, this development cycle focused on polishing functionalities and building on top of preexisting features. The variety of improvements is sure to make our diverse user base and community excited to upgrade to QField 3.6.

Main highlights

One of the most noticeable improvement in this version is the addition of “map preview rendering”. QField now renders partial map content immediately beyond the edge of the screen, offering a much nicer experience when panning around as well as zooming in and out. Long-time QGIS users will recognise the behaviour, and we’re delighted to bring this experience to the field

This upgrade was the foundation upon which we built the following enhancement: as of QField 3.6, using the “lock to position” mode now keeps your position at the very center of the screen while the canvas slips through smoothly. This greatly improves the usability of the function as your eyes never need to spend time locating the position within the screen: it’s dead center and it stays there!

Reminder, the “lock to position” mode is activated by clicking on the bottom-right positioning button, with the button’s background turning blue when the mode is activated.

The improvements did not stop there. Panning and zooming around used to drop users out of the lock mode immediately. While this had its upsides, it also meant that simple scale adjustments to try and view more of the map as it follows the position was not possible. With QField 3.6, the lock has been hardened. Moving the map around will temporarily disable the lock, with a visual countdown embedded within a toast message informs users of when the lock will return. An action button to terminate the lock is located within the toaster to permanently leave the mode.

Moving on to QFieldCloud, this cycle saw tons of improvements. To begin with, it is now possible to rely on shared datasets across multiple cloud projects. Known as localised data paths in QGIS, this functionality enables users to reduce storage usage by storing large datasets in QFieldCloud only once, serving multiple cloud projects, and also easing the maintenance of read-only datasets that require regular updates.

QFieldSync users will see a new checkbox when synchronising their projects, letting them upload shared datasets onto QFieldCloud.

Furthermore, QField has introduced a new cloud project details view to provide additional details on QFieldCloud-hosted projects before downloading them to devices. The new view includes a cloud project thumbnail, more space for richer description text, including interactive hyperlinks, and author details, as well as creation and data update timestamps. Finally, the view offers a QR code, which allows users to scan it quickly and access cloud projects, provided they have the necessary access permission. Distributing a public project has never been easier!

Beyond that, tons more has made its way into QField, including map layer notes viewable through a legend badge in the side dashboard, support for feature identification on online raster layers on compatible WMS and ArcGIS REST servers, atlas printing of a relationship’s child feature directly within the parent feature form, and much more. There’s something for everybody out there.

Focus on feature form polishing

This new version of QField coincides with the release of XLSForm Converter, a new QGIS plugin created by OPENGIS.ch’s very own ninjas. As its title implies, the plugin converts an XLSForm spreadsheet file (.xls, .xlsx, .ods) into a full-fledged QGIS project ready to be used in QField with a pre-configured survey layer matching the content of the provided XLSForm.

This was a golden opportunity to focus on polishing QField’s feature form. As a result, advanced functionalities such as data-driven editable flag and label attribute properties are now supported. In addition, tons of paper-cut bugs, visual inconsistencies, and UX shortcomings have been addressed. Our favourite one might just be the ability to drag the feature addition drawer’s header up and down to toggle its full-screen state

The latest releases of MovingPandas and Trajectools come with many “under the hood” changes that aim to make your movement analytics faster:

1. Instead of immediately creating a GeoPandas GeoDataFrame and populating the geometry column with Point objects, MovingPandas now has “lazy geometry column creation” that holds off on this operation until / if the geometries are actually needed. This way, for many operations, no geometry objects have to be generated at all.
2. MovingPandas TrajectorySplitters now support parallel processing and Trajectools uses parallel processing whenever available (e.g. for adding speed & direction metrics, detecting stops, splitting trajectories).
3. When a minimum length is specified for trajectories, MovingPandas now avoids computing the total trajectory length and, instead, immediately stops once the threshold value has been reached (“early skip”).
4. Trajectools now offers the option to skip computation of movement metrics (speed & direction). This way, we can skip unnecessary computations and leverage the lazy geometry column creation, wherever applicable.

Let’s have a look at some example performance measurements!

Example 1: MovingPandas ValueChangeSplitter

The ValueChangeSplitter splits trajectories when it detects a value change in the specified column. This is useful, for example, to split up public trajectories that contain a “next_stop” column.

The following graph shows ValueChangeSplitter runtimes for different minimum trajectory length settings (from 0 to 1km, 100km, and 10,000km):

We see that the new, lazy geometry column initialization outperforms the old original code in all cases (e.g. 57% runtime reduction for 1km), except for the worst-case scenario, when the original implementation discards all trajectories as too short right from the start. (For most use cases, min_length will be set to rather small values to avoid creation of undesired short trajectory fragments, similar to sliver polygons in classic geometry operations.)

Additionally, we can engage multiprocessing by setting the n_processes parameter, e.g. to the number of CPUs to achieve further speedup:

Example 2: Trajectools

By applying all above-mentioned speedup techniques, Trajectools is now considerably faster. For example, the following runtime reductions can be achieved by deactivating the “Add movement metrics (speed, direction)” option in the algorithm dialog:

• Create trajectories: 62%
• Spatiotemporal generalization (TDTR): 78%
• Temporal generalization: 81%
• Split trajectories at stops: 53%

I have also updated the default trajectory points output style. It now uses a graduated renderer to visualize the speed values (if they have been calculated) instead of the previously used data-defined override. This makes the style faster to customize and provides a user-friendly legend:

For more infos, have a look at:

• MovingPandas 0.22 release notes
• Trajectools 2.6 release notes

Enjoy the latest performance increases!

Sorry, this entry is only available in French.