Tag: gis

Finally it’s here: Jupyter notebooks inside QGIS. I don’t know about you but I’ve been hoping for someone to get around to doing this for quite a while.

Qiusheng Wu published the first version of the Notebook plugin on 26 Dec 2025. Late Christmas present?!

For the setup, there’s a handy tutorial by Hans van der Kwast and, additionally, Qiusheng published an intro video:

Development is going fast (version 0.3.0 at the time of writing) so there will be new features when you install / update the plugin compared to both the tutorial and the video.

The user interface is pretty stripped down with just a few buttons to add new code or markdown cells and to run them. And there is a neat drop-down menu with all kinds of ready-made code snippets to get you started:

For other functionalities, for example, to delete cells, you need to right-click on the cell to access the function through the context menu. And, as far as I can tell, there is currently no way to rearrange cells (moving them up or down).

I also haven’t quite understood yet what kinds of outputs are displayed and which are not because – quite often – the cell output just stays empty, even though the same code generates output on the console:

Some of the plugin settings I would have liked to experiment with, such as adjusting the font size or enabling line numbers, don’t seem to work yet. So a little more patience seems to be necessary.

I’ll definitely keep an eye on this one :)

The journey continues: QgsArrowIterator is now merged! This makes it possible to iterate over QgsFeatures as Arrow batches.

This is where we are now, quoting Dewey Dunnington:

import geopandas
from nanoarrow.c_array import allocate_c_array
import qgis
from qgis.core import QgsVectorLayer

# Create a vector layer
layer = QgsVectorLayer("tests/testdata/zonalstatistics/polys.shp", "layer_name", "ogr")
schema = qgis.core.QgsArrowIterator.inferSchema(layer)

it = qgis.core.QgsArrowIterator(layer.getFeatures())
it.setSchema(schema, 1)

c_array = allocate_c_array()
schema.exportToAddress(c_array.schema._addr())
it.nextFeatures(5, c_array._addr())

print(geopandas.GeoDataFrame.from_arrow(c_array))
#> lev3_name                                           geometry
#> 0    poly_1  MULTIPOLYGON (((100.37934 -0.96049, 100.37934 ...
#> 1    poly_2  MULTIPOLYGON (((100.37944 -0.96044, 100.37955 ...
#> 2    poly_3  MULTIPOLYGON (((100.37938 -0.96049, 100.37949 ...

print(geopandas.read_file("tests/testdata/zonalstatistics/polys.shp"))
#> lev3_name                                           geometry
#> 0    poly_1  POLYGON ((100.37934 -0.96049, 100.37934 -0.960...
#> 1    poly_2  POLYGON ((100.37944 -0.96044, 100.37955 -0.960...
#> 2    poly_3  POLYGON ((100.37938 -0.96049, 100.37949 -0.960...

Further improvements are already being planned. To quote from the ticket:

“The final state after this improvement would be a compact way for Arrow Python consumers like GeoPandas to ergonomically consume a layer. Maybe:

geopandas.GeoDataFrame.from_arrow(qgis_layer_object)

Or maybe:

geopandas.GeoDataFrame.from_arrow(qgis_layer_object.getArrowStream())

Looking forward to seeing this develop further.

The last time I preprocessed the whole GeoLife dataset, I loaded it into PostGIS. Today, I want to share a new workflow that creates a (Geo)Parquet file and that is much faster.

The dataset (GeoLife)

“This GPS trajectory dataset was collected in (Microsoft Research Asia) Geolife project by 182 users in a period of over three years (from April 2007 to August 2012). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude and altitude. This dataset contains 17,621 trajectories with a total distance of about 1.2 million kilometers and a total duration of 48,000+ hours. These trajectories were recorded by different GPS loggers and GPS-phones, and have a variety of sampling rates. 91 percent of the trajectories are logged in a dense representation, e.g. every 1~5 seconds or every 5~10 meters per point.”

The GeoLife GPS Trajectories download contains 182 directories full of .plt files:

Basically, CSV files with a custom header:

Creating the (Geo)Parquet using DuckDB

DuckDB installation

Following the official instructions, installation is straightforward:

curl https://install.duckdb.org | sh

From there, I’ve been using the GUI which we can launch using:

duckdb -ui

The spatial extension is a DuckDB core extension, so it’s readily available. We can create a spatial db with:

ATTACH IF NOT EXISTS ':memory:' AS memory;
INSTALL spatial;
LOAD spatial;

Reading a spatial file is as simple as:

SELECT * 
FROM '/home/anita/Documents/Codeberg/trajectools/sample_data/geolife.gpkg'

thanks to the GDAL integration.

But today, we want to do to get a bit more involved …

DuckDB SQL magic

The issues we need to solve are:

1. Read all CSV files from all subdirectories
2. Parse the CSV, ignoring the first couple of lines, while assigning proper column names
3. Assign the CSV file name as the trajectory ID (because there is no ID in the original files)
4. Create point geometries that will work with our GeoParquet file
5. Create proper datetimes from the separate date and time fields

Luckily, DuckDB’s read_csv function comes with the necessary features built-in. Putting it all together:

CREATE OR REPLACE TABLE geolife AS 
SELECT 
  parse_filename(filename, true) as vehicle_id, 
  strptime(date||' '||time, '%c') as t, 
  ST_Point(lon, lat) as geometry -- do NOT use ST_MakePoint
FROM read_csv('/home/anita/Documents/Geodata/Geolife/Geolife Trajectories 1.3/Data/*/*/*.plt',
    skip=6,
    filename = true, 
    columns = {
        'lat': 'DOUBLE', 
        'lon': 'DOUBLE', 
        'ignore': 'INT', 
        'alt': 'DOUBLE', 
        'epoch': 'DOUBLE', 
        'date': 'VARCHAR',
        'time': 'VARCHAR'
    });

It’s blazingly fast:

I haven’t tested reading directly from ZIP archives yet, but there seems to be a community extension (zipfs) for this exact purpose.

Ready to QGIS

GeoParquet files can be drag-n-dropped into QGIS:

I’m running QGIS 3.42.1-Münster from conda-forge on Linux Mint.

Yes, it takes a while to render all 25 million points … But you know what? It get’s really snappy once we zoom in closer, e.g. to the situation in Germany:

Let’s have a closer look at what’s going on here.

Trajectools time

Selecting the 9,438 points in this extent, let’s compute movement metrics (speed & direction) and create trajectory lines:

Looks like we have some high-speed sections in there (with those red > 100 km/h streaks):

When we zoom in to Darmstadt and enable the trajectories layer, we can see each individual trip. Looks like car trips on the highway and walks through the city:

That looks like quite the long round trip:

Let’s see where they might have stopped to have a break:

If I had to guess, I’d say they stayed at the Best Western:

Conclusion

DuckDB has been great for this ETL workflow. I didn’t use much of its geospatial capabilities here but I was pleasantly surprised how smooth the GeoParquet creation process has been. Geometries are handled without any special magic and are recognized by QGIS. Same with the timestamps. All ready for more heavy spatiotemporal analysis with Trajectools.

If you haven’t tried DuckDB or GeoParquet yet, give it a try, particularly if you’re collaborating with data scientists from other domains and want to exchange data.

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!

The “Security project for QGIS” is now public ! Pledge now !

The goal of this project is to mutualize funding to improve QGIS security to the highest levels.

Oslandia and other involved partners, especially OPENGIS.ch are OpenSource “pure players” and main contributors to QGIS. This project is an initiative by Oslandia and is endorsed by the QGIS.org association. We work closely with the community of developers, users and stakeholders of QGIS. This project involves QGIS core committers willing to advance QGIS security.

Global context

New regulations like NIS2 and CRA in Europe, as well as other international or local regulations will be activated within the next couple of years. They require software and software producers to improve their cybersecurity practices. OpenSource softwares, while usually having a special treatment, are concerned too. Estimated costs of CRA impact on an opensource project amounts to +30%.

As for QGIS, we consider that the project stays behind what would be sufficient to comply with these regulations. We also do not fulfill requirements coming from our end-users, in terms of overall software quality regarding security, processes in place to ensure trust in the supply chain, and overall security culture in the project.

We have been discussing this topic with clients having large deployments of QGIS and QGIS server, and they stressed the issue, stating that cybersecurity is one of their primary concerns, and that they are willing to see the QGIS project move forward in this area as soon as possible. QGIS faces the risk of IT departments blocking QGIS installations if they consider the project not having enough consideration for security.

Also, requests to security@qgis.org have grown significantly.

Project goals

Oslandia, with other partners and backed by clients and end-users, launch the “Security project for QGIS” : we identified key topics where security improvements can be achieved, classified them, and created work packages to work on, with budget estimations.

• The main goal is simple : raise the cybersecurity level for the QGIS project
• Fulfill cybersecurity requirements from regulations and end-users
• Make QGIS an example of security-aware OpenSource project, helping other OSGeo projects to improve

While QGIS and QGIS server are the main components on which this project focus, improving QGIS security as a whole also needs to consider underlying libraries ( e.g. GDAL/OGR, PROJ, GEOS…).

This project is a specific effort to raise the level of security of QGIS. Maintaining security in the long term will need further efforts, and we encourage you to sponsor QGIS.org, becoming a sustaining member of QGIS.

Memory safety, signing binaries, supply chain management, contributing processes, plugin security, cybersecurity audits and much more topics are included in this project. You can see all items as well as work packages on the dedicated website :

https://security.qgis.oslandia.com

Project organization – Pledge !

Any organization interested in improving QGIS security can contribute to funding the project. We are looking for an estimated total amount of 670K€, divided into 3 work packages Pledge now !

Once funded, Oslandia and partners will start working on Work Package 1 in 2025. We intend to work closely with the QGIS community, QGIS.org, interested partners and users. Part of the work are improvements over the current system, other require changes to processes or developer’s habits. Working closely with the user and developer’s community to raise our security awareness is fully part of the project.

We will deliver improvements in 2025 and until 2027. You can see the full list of topics, work packages and estimated budget on the project’s dedicated page : security.qgis.oslandia.com . You are invited to participate, but also to help spread the word and recruit other contributors !

We want to especially thank Orange France for being a long-time supporter of OpenSource in general and QGIS particularly, and the first backer of the Security Project for QGIS !

Should you have any question, or need further material to convince other stakeholders, get in touch !

Let’s not bury the lead here: the long-awaited capability to track position while QField is in the background or the device is locked has arrived in this brand-new version of QField. This feels like a magical moment, so we settled for a fantastical forest for our release name.

Main highlights

As highlighted above, QField 3.5 has unlocked background position tracking on the Android platform. This allows users to keep track of their positions even as they put QField in the background to conduct other tasks on their devices. It also means that tracking has become far more battery efficient, as users can lock/suspend their phones and tablets for long periods while QField continues to collect and track positions. On top of it all, this will work out of the book with internal GNSS as well as external high-precision GNSS devices.

This is a long-requested functionality for QField, and we couldn’t be prouder to deliver it to our hundreds of thousands of Android users. Big thanks to Groupements forestiers Québec , Biotope , and Terrex Seismic, who jointly sponsored the development.

Moving on to the next major feature added to this new version. Users can now easily import folders from WebDAV services and subsequently upload and download content to that remote folder within QField itself. This functionality eases friction on Android and iOS platforms where storage access is heavily regulated. This implementation highlights our commitment to providing QField users with the freedom they need to build their workflows; thanks to Prona Romandie , AgaricIG , and Oslandia for commissioning this work.

It’s important to note that the WebDAV functionality does not provide data synchronization. The download and upload operations will overwrite datasets stored locally or remotely. For users in need of synchronization and smooth project distribution, QFieldCloud is the way to go . With this new version of QField, downloading large datasets from QFieldCloud has become much more reliable, especially on devices with low memory.

Last but not least, QField has gained support for project-configured grid decoration. When activated, a grid is overlayed on top of the map canvas, which will dynamically render while panning and zooming around. The grid is configured and activated while setting up projects within QGIS itself.

Pro tip: this functionality can replace heavy grid datasets when covering a large dataset, something to consider when trying to optimize projects’ storage size. Big thanks to Oester Messtechnik GmbH for supporting the implementation of this fourth decoration following the arrival of title, copyright, and image decorations in earlier releases.

Other improvements in this release include “forward” angle snapping to digitize perfectly angled polygons, pinch gesture-driven feature rotation, and a new print template which unlocks printing of map canvas to PDF even when their projects have no layouts defined.

Plugin-specific improvements

One of the main additions to QField’s plugin framework is the capability to integrate custom results into the search bar. Thanks to Kanton Basel-Landschaft for supporting the development, users can enjoy OpenStreetMap Nominatim search result integration by installing this plugin (instructions available on the repository). This integration also opens up many new possibilities, such as enabling plugins to send prompts to AI, just like this plugin does.

Other noteworthy improvements include shipping Quick3D QML modules, which allow authors to develop 3D overlays, a new API to customize QField’s colour appearance and a new mechanism for plugins to add a configuration button within the plugin manager.

Users and plugin authors can expect an exciting year ahead as the QField plugin framework continues to grow with new functionalities and improvements. Watch this space!

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 !

QField 3.4 is out, and it won’t disappoint. It has tons of new features that continue to push the limits of what users can do in the field.

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 we have 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!

Oslandia is the main partner of OPENGIS.ch around QField. We are proud today to forward the announcement of the new QField release 3.3 “Darién”. This release introduces a brand new plugin framework that empowers users to customize and add completely new functionalities to their favourite field application.

The plugin framework comes with other new features and improvements for this release, detailed below.

Main highlights

One of the biggest feature additions of this version is a brand new drawing tool that allows users to sketch out important details over captured photos or annotate drawing templates. This was a highly requested feature, which is brought to all supported platforms (Android, iOS, Windows, macOS, and, of course, Linux) with the financial support of the Swiss QGIS user group.

Also landing in this version is support for copying and pasting vector features into and from the clipboard. This comes in handy in multiple ways, from providing a quick and easy way to transfer attributes from one feature to another through matching field names to pasting the details of a captured feature in the field into a third-party messenger, word editing, or email application. Copying and pasting features can be done through the feature form’s menu as well as long pressed over the map canvas. Moreover, a new feature-to-feature attributes transfer shortcut has also been added to the feature form’s menu. Appreciation to Switzerland, Canton of Lucerne, Environment and Energy for providing the funds for this feature.

The feature form continues to gain more functionalities; in this version, the feature form’s value map editor widget has gained a new toggle button interface that can help fasten data entry. The interface replaces the traditional combo box with a series of toggle buttons, lowering the number of taps required to pick a value. The German Archaeological Institut – KulturGutRetter sponsored this feature.

Other improvements in the feature form include support for value relation item grouping and respect for the vector layer attributes’ « reuse last entered value » setting.

Finally, additional features include support for image decoration overlay, a new interface to hop through cameras (front, back, and external devices) for the ‘non-native’ camera, the possibility to disable the 3-finger map rotation gesture, and much more.

User experience improvements

Long-time users of QField will notice the new version restyling of the information panels such as GNSS positioning, navigation, elevation profile, and sensor data. The information is now presented as an overlay sitting on top of the map canvas, which increases the map canvas’ visibility while also achieving better focus and clarity on the provided details. With this new version, all details, including altitude and distance to destination, respect user-configured project distance unit type.

The dashboard’s legend has also received some attention. You can now toggle the visibility of any layer via a quick tap on a new eye icon sitting in the legend tree itself. Similarly, legend groups can be expanded and collapsed directly for the tree. This also permits you to show or hide layers while digitizing a feature, something which was not possible until now. The development of these improvements was supported by Gispo and sponsored by the National Land Survey of Finland.

Plugin framework

QField 3.3 introduces a brand new plugin framework using Qt’s powerful QML and JavaScript engine. With a few lines of code, plugins can be written to tweak QField’s behaviour and add new capabilities. Two types of plugins are possible: app-wide plugins as well as project-scoped plugins. To ensure maximum ease of deployment, plugin distribution has been made possible  through QFieldCloud! Amsa provided the financial contribution that brought this project to life.

Our partner OPENGIS.ch will soon offer a webinar to discover how QField plugins can help your field (and business) workflows by allowing you to be even more efficient in the field.

Users interested in authoring plugins or better understanding the framework, can already visit the dedicated documentation page and a sample plugin implementation sporting a weather forecast integration.

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

QField 3.3 has been released, and with it, we are proud to introduce a brand new plugin framework that empowers users to customize and add completely new functionalities to their favourite field application. That’s on top of a bunch of new features and improvements added during this development cycle. What preceded this moment was just the beginning!

Main highlights

One of the biggest feature additions of this version is a brand new drawing tool that allows users to sketch out important details over captured photos or annotate drawing templates. This was a highly requested feature, which we are delighted to bring to all supported platforms (Android, iOS, Windows, macOS, and, of course, Linux) with the financial support of the Swiss QGIS user group .

Also landing in this version is support for copying and pasting vector features into and from the clipboard. This comes in handy in multiple ways, from providing a quick and easy way to transfer attributes from one feature to another through matching field names to pasting the details of a captured feature in the field into a third-party messenger, word editing, or email application. Copying and pasting features can be done through the feature form’s menu as well as long pressed over the map canvas. If copy pasting ain’t your style, a new feature-to-feature attributes transfer shortcut has also been added to the feature form’s menu. Appreciation to Switzerland, Canton of Lucerne, Environment and Energy for providing the funds for this feature.

The feature form continues to gain more functionalities; in this version, the feature form’s value map editor widget has gained a new toggle button interface that can help fasten data entry. The interface replaces the traditional combo box with a series of toggle buttons, lowering the number of taps required to pick a value. If you enjoy this as much as we do, send a virtual thanks to German Archaeological Institut - KulturGutRetter , which sponsored this feature.

Other improvements in the feature form include support for value relation item grouping and respect for the vector layer attributes’ “reuse last entered value” setting.

Finally, additional features that are sure to please include support for image decoration overlay, a new interface to hop through cameras(front, back, and external devices) for the ‘non-native’ camera , the possibility to disable the 3-finger map rotation gesture, and much more .

User experience improvements

Long-time users of QField will notice the new version restyling of the information panels such as GNSS positioning, navigation, elevation profile, and sensor data. The information is now presented as an overlay sitting on top of the map canvas, which increases the map canvas’ visibility while also achieving better focus and clarity on the provided details. While revisiting these information panels, we’ve made sure all details, including altitude and distance to destination, respect user-configured project distance unit type.

The dashboard’s legend has also received some attention. You can now toggle the visibility of any layer via a quick tap on a new eye icon sitting in the legend tree itself. Similarly, legend groups can be expanded and collapsed directly for the tree. This also permits you to show or hide layers while digitizing a feature, something which was not possible until now. The development of these improvements was supported by Gispo and sponsored by the National Land Survey of Finland .

Plugin framework

Last but far away from least, QField 3.3 introduces a brand new plugin framework using Qt’s powerful QML and JavaScript engine. With a few lines of code, plugins can be written to tweak QField’s behaviour and add breathtaking capabilities. Two types of plugins are possible: app-wide plugins as well as project-scoped plugins. To ensure maximum ease of deployment, we have enabled project plugin distribution through QFieldCloud ! We extend our heartfelt thanks to Amsa for the financial contribution that brought this incredible project to life.

Stay tuned for an upcoming webinar and a dedicated post that will dive into how QField plugins can revolutionize your field (and business) workflows by allowing you to be even more efficient in the field.

Users interested in authoring plugins or better understanding the framework can already visit the dedicated documentation page , a sample plugin implementation sporting a weather forecast integration and our latest blog article .