QGIS 3.36 is round the corner and as usual, there will be several new exciting features with the new release. Below is the list of features our team has added to the new release. This was made possible by generous funding from clients.

Render point clouds as a surface in 2D map views

Point clouds are rendered as individual points by default. Depending on the zoom level and density of the points, you might not be able to get a full picture of the map.

Rendering points as surface enables you to have a better understanding of the data before trying to do any analysis or processing. This has been possible in 3D map views for a couple of QGIS releases, now we have added the functionality also in 2D map views.

The feature generates a surface using triangulation on-the-fly from the points using the same symbology. Below you can see the difference between a point cloud rendered as individual points and rendered as a surface:

The good news is that rendering as a surface also works well with global map shading, allowing users to get a nice hillshade:

To enable this feature, you need to check the option for Render as a Surface (Triangulate) under the Layer Styling panel.

Pro-tip: if the on-the-fly rendering as a surface takes too long to render, try increasing the Maximum error: for example 0.6 mm instead of the default 0.3 mm.

Flexible styling of classes

Previously, point cloud data visualisation in QGIS was limited to rendering all points with a uniform size and opacity. This made it difficult to differentiate between different point classes and highlight specific features of interest. To address this issue, we have introduced a new feature that allows users to customise the point size and opacity for each point cloud data class. This provides a flexible way for visualising point cloud data, allowing users to highlight specific point classes, e.g. by increasing the point size.

Set 3D map view extent in 2D map

Effectively navigating and visualising large-scale 3D datasets can be challenging on PCs with limited resources. To address this issue, we introduced a new feature that allows users to interactively limit the 3D scene, enabling them to focus on specific areas of interest. This new feature, conveniently accessible from the toolbar, eliminates the need for tedious manual entry of coordinates for bounding boxes. Instead, users can simply drag and draw a box around the desired area, instantly restricting the 3D scene to that specific extent. This interactive approach significantly enhances the user experience and streamlines the process of exploring and analysing 3D data:

Python API for 3D views

Creating and viewing 3D maps in QGIS with the correct camera location and angle, scene tilt, light, and other parameters can be a time-consuming and error-prone process. This is because it requires users to manually adjust these settings, often through trial and error. However, with the introduction of the new 3D map view API in QGIS, Python plugins can now automate this process, making it much easier, consistent and more efficient to create high-quality 3D maps that are visually appealing and informative.

# List all open 3D map canvases
iface.mapCanvases3D()
# [<qgis._3d.Qgs3DMapCanvas object at 0x7f23491b5e10>]
canvas = iface.mapCanvases3D()[0]

Now let’s try something more complicated:

# Let's change some settings!
ms = canvas.mapSettings()
ms.setEyeDomeLightingEnabled(True)
ms.setBackgroundColor(QColor('beige'))
ms.setTerrainMapTheme('dtm')
ms.setFieldOfView(110)

# Move the camera to look at specific map coordinates in layer's CRS
cam = canvas.cameraController()
mapPoint = QgsVector3D(-498175.92, -1205400.58, 210)
worldPoint = ms.mapToWorldCoordinates(mapPoint)
cam.setLookingAtPoint(worldPoint, 60, 45, 100)

# Create four new 3D map views
c1 = iface.createNewMapCanvas3D('South View')
c2 = iface.createNewMapCanvas3D('West View')
c3 = iface.createNewMapCanvas3D('North View')
c4 = iface.createNewMapCanvas3D('East View')

# Apply settings to all open 3D map views
for canvas in iface.mapCanvases3D():
	canvas.mapSettings().setEyeDomeLightingEnabled(True)

# Define a camera pose to update the views' cameras
pose = QgsCameraPose()
pose.setCenterPoint(QgsVector3D(0, 210, 0))  # This is in 3D world coordinates
pose.setDistanceFromCenterPoint(100)
pose.setPitchAngle(75)  # Tilt the camera by 75 degrees
pose.setHeadingAngle(0)  # Looking towards North
c1.cameraController().setCameraPose(pose)
pose.setHeadingAngle(90)  # Towards East
c2.cameraController().setCameraPose(pose)
pose.setHeadingAngle(180)  # Towards South
c3.cameraController().setCameraPose(pose)
pose.setHeadingAngle(270)  # Towards West
c4.cameraController().setCameraPose(pose)

# We can set the 3D map views 2D extent to always match the main 2D canvas one
# Our 3D views get updated while zooming/panning the main 2D canvas
canvas = iface.mapCanvas()
canvas.extentsChanged.connect(lambda :c1.mapSettings().setExtent(canvas.extent()))
canvas.extentsChanged.connect(lambda :c2.mapSettings().setExtent(canvas.extent()))
canvas.extentsChanged.connect(lambda :c3.mapSettings().setExtent(canvas.extent()))
canvas.extentsChanged.connect(lambda :c4.mapSettings().setExtent(canvas.extent()))

More point clouds attributes

LAS/LAZ/COPC point clouds have a classificationFlags attribute that stores four types of information (Synthetic, Keypoint, Withheld, and Overlap) in a single value. This saves space, but it makes it difficult to use the information for styling or filtering, as you need to write complex expressions.

To make it easier to use, we are following the approach introduced in PDAL 2.6: the classificationFlags attribute gets replaced with four separate attributes: Synthetic, Keypoint, Withheld, and Overlap. This will make it easier to include these attributes in styling and filtering expressions.

Performance enhancement for rendering

To improve the performance of point cloud rendering in QGIS, we introduced a new caching system to minimise the need for repeated decompression of LAS node data while panning or zooming. This caching mechanism efficiently stores decompressed points for each unique combination of layer URI, node, requested attributes, filter extent, and filter expression. This enables rapid rendering of previously cached nodes, significantly enhancing the overall rendering performance in 2D and 3D maps.

Performance can vary depending on actual data, but on a local sample COPC file, it renders 7 times faster with this change.

Labels for mesh layer

Viewing mesh data has been possible through styling, plotting or using the Identify tool. But now you can also create labels on mesh surfaces or vertices similar to vector layers.

To display labels for your mesh data, simply open the styling panel and enable labels for:

• Labels on Vertices
• Labels on Surfaces

Below is an example of mesh Z values at vertices (yellow) and mesh areas at cell centre (purple):

Want more changes in QGIS?

Do you want to see more improvements and new features in QGIS? Do not hesitate to contact us to discuss your requiremnets.

Earlier this year, in collaboration with North Road we were awarded a grant from Cesium to introduce 3D tiles support in QGIS. The feature was developed successfully and shipped with QGIS 3.34.

In this blog post, you can read more about how to work with this feature, where to get data and how to display your maps in 2D and 3D. For a video demo of this feature, you can watch Nyall Dawson’s presentation on Youtube.

What are 3D tiles?

3D tiles are a specification for streaming and rendering large-scale 3D geospatial datasets. They use a hierarchical structure to efficiently manage and display 3D content, optimising performance by dynamically loading appropriate levels of detail. This technology is widely used in urban planning, architecture, simulation, gaming, and virtual reality, providing a standardised and interoperable solution for visualising complex geographical data.

Examples of 3D tiles:

3D tiles in QGIS

To be able to use 3D tiles in QGIS, you need to have QGIS 3.34 or later. You can add a new connection to a 3D tile service from within the Data Source Manager under Scene:

Alternatively, you can add the service from your Browser Panel:

To test the feature, you can use the following 3D tiles service:

Name: 3D Tiles example
URL: https://pelican-public.s3.amazonaws.com/3dtiles/agi-hq/tileset.json

You can then add the map from the newly generated connection to QGIS:

By default, the layer is styled using texture, but you can change it to see the wireframe mesh behind the scene:

You can change the mesh fill and line symbols similar to the vector polygons. Alternatively, you can use texture colors. This will render each mesh element with the average value of the full texture. This is ideal when dealing with a large dataset and want to get a quick overview of the data:

To view the data in 3D, you can open a new 3D map. Similar to 2D map, by zooming in/out, finer resolution tiles will be fetched and displayed:

Using data from Cesium ion

Cesium ion is a cloud-based platform for managing and streaming 3D geospatial data. It simplifies data management, visualisation, and sharing.

To add 3D tiles from Cesium ion, you need to first sign up to their service here: https://ion.cesium.com/tokens

Under Asset Depot, you will see a catalogue of publicly available datasets. You can also upload your own 3D models (such as OBJ or PLY), georeference them and get them converted to 3D tiles.

You can also add one of the existing tile service under https://ion.cesium.com/assetdepot and select the tile service and then click on Add to my assets:

You can use the excellent Cesium ion plugin by North Road from the QGIS repository to add the data to QGIS:

Working with Google 3D data

In addition to accessing Google Photorealistic 3D tiles from Cesium ion, you can also add the tiles directly in QGIS. First you will need to follow the instructions below and obtain API keys for 3D tiles: https://developers.google.com/maps/documentation/tile/cloud-setup

During the registration process, you will be asked to add your credit card details. Currently (November 2023), they do not charge you for using the service.

Once you have obtained the API key, you can add Google tiles using the following connection details:

Notes and remarks

• Adjusting map extents for large scenes

When dealing with large scenes, map extents should be set to a smaller area to be able to view it in 3D. This is the current limitation of QGIS 3D maps as it cannot handle scenes larger than 500 x 500 km.

To change the map extent, you can open Project Properties and under View Settings change the extent. In the example below, the map extent has been limited only to a part of London, so we can view Google Photorealistic tiles in the 3D map without rendering issues.

• Network cache size

If you are handling a large dataset, it is recommended to increase network cache size to 1 GB or more. The default value in QGIS is much lower and it results in slower rendering of the data.

• Overlaying other 3D data

When you try to overlay other data sets on top of a global 3D tiles, the vertical datum might not match and hence you will see the data in the wrong place in a 3D map. To fix the issue, you may need to use elevation offsetting to shift the data along the Z axis under Layer Properties:

Future works

This is the first implementation of the 3D tiles in QGIS. For the future, we would like to add more features for handling and creation of the 3D tiles. Our wishlist in no particular order is:

• Globe view: QGIS 3D cannot handle large scenes or unprojected views.
• More advanced styling of meshes: as an example, users will be able to create their own style.
• 3D In-door navigation: as an example users will be able to navigate inside buildings and potentially it will bring BIM data closer to QGIS
• Generation of 3D tiles inside QGIS: adding a processing tool in QGIS to generate 3D Tiles from your map data.

If you would like to see those features in QGIS and want to fund the efforts, do not hesitate to contact us.

Join this webinar to learn more about the new features in QGIS to process LiDAR data:

Date: Monday, June 26, 2023 at 14:00 GMT

Duration: 30 minutes + 15 minutes Q&A session

Speaker: Martin Dobias, CTO at Lutra Consulting with more than 15 years of QGIS development experience

Description

Point clouds are an increasingly popular data type thanks to the decreasing cost of their acquisition through lidar surveys and photogrammetry. On top of that, more and more national mapping agencies release high resolution point cloud data (spanning large areas and consisting of billions of points), unlocking many new use cases.

This webinar will summarize the latest QGIS release 3.32 and the addition of tools for point cloud analysis right from QGIS Processing toolbox: clip, filter, merge, export to raster, extract boundaries and more - all backed by PDAL library that already ships with QGIS, without having to rely on third party proprietary software.

This work was made possible by the generous donations to our crowdfunding.

Live on Monday, June 26, 2023 at 14:00 GMT

Add it to your calendar!

As a part of our crowdfunding campaign we have introduced a new method to handle a large number of point cloud files. In this article, we delve into the technical details of the new format, rationale behind our choice and how you can create, view and process virtual point cloud files.

Rationale

Lidar surveys of larger areas are often multi-terabyte datasets with many billions of points. Having such large datasets represented as a single point cloud file is not practical due to the difficulties of storage, transfer, display and analysis. Point cloud data are therefore typically stored and distributed split into square tiles (e.g. 1km x 1km), each tile having a more manageable file size (e.g. ~200 MB when compressed).

Tiling of data solves the problems with size of data, but it introduces issues when processing or viewing an area of interest that does not fit entirely into a single tile. Users need to develop workflows that take into account multiple tiles and special care needs to be taken to deal with data near edges of tiles to avoid unwanted artefacts in outputs. Similarly, when viewing point cloud data, it becomes cumbersome to load many individual files and apply the same symbology.

Here is an example of several point cloud tiles loaded in QGIS. Each tile is styled based on min/max Z values of the tile, creating visible artefacts on tile edges. The styling has to be adjusted for each layer separately:

Virtual Point Clouds

In the GIS world, many users are familiar with the concept of virtual rasters. A virtual raster is a file that simply references other raster files with actual data. In this way, GIS software then treats the whole dataset comprising many files as a single raster layer, making the display and analysis of all the rasters listed in the virtual file much easier.

Borrowing the concept of virtual rasters from GDAL, we have introduced a new file format that references other point cloud files - and we started to call it virtual point cloud (VPC). Software supporting virtual point clouds handles the whole tiled dataset as a single data source.

At the core, a virtual point cloud file is a simple JSON file with .vpc extension, containing references to actual data files (e.g. LAS/LAZ or COPC files) and additional metadata extracted from the files. Even though it is possible to write VPC files by hand, it is strongly recommended to create them using an automated tool as described later in this post.

On a more technical level, a virtual point cloud file is based on the increasingly popular STAC specification (the whole file is a STAC API ItemCollection). For more details, please refer to the VPC specification that also contains best practices and optional extensions (such as overviews).

Virtual Point Clouds in QGIS

We have added support for virtual point clouds in QGIS 3.32 (released in June 2023) thanks to the many organisations and individuals who contributed to our last year’s joint crowdfunding with North Road and Hobu. The support in QGIS consists of three parts:

1. Create virtual point clouds from a list of individual files
2. Load virtual point clouds as a single map layer
3. Run processing algorithms using virtual point clouds

Those who prefer using command line tools, PDAL wrench includes a build_vpc command to create virtual point clouds, and all the other PDAL wrench commands support virtual point clouds as the input.

Using Virtual Point Clouds

In this tutorial, we are going to generate a VPC using the new Processing algorithm, load it in QGIS and then generate a DTM from terrain class. You will need QGIS 3.32 or later for this. For the purpose of this example, we are using the LiDAR data provided by the IGN France data hub.

In QGIS, open the Processing toolbox panel, search for the Build virtual point cloud (VPC) algorithm ((located in the Point cloud data management group):

In the algorithm’s window, you can add point cloud layers already loaded in QGIS or alternatively point it to a folder containing your LAZ/LAS files. It is recommended to also check the optional parameters:

• Calculate boundary polygons - QGIS will be able to show the exact boundaries of data (rather than just rectangular extent)

• Calculate statistics - will help QGIS to understand ranges of values of various attributes

• Build overview point cloud - will also generate a single “thinned” point cloud of all your input data (using only every 1000th point from original data). The overview point cloud will be created next to the VPC file - for example, for mydata.vpc, the overview point cloud would be named mydata-overview.copc.laz

After you set the output file and start the process, you should end up with a single VPC file referencing all your data. If you leave the optional parameters unchecked, the VPC file will be built very quickly as the algorithm will only read metadata of input files. With any of the optional parameters set, the algorithm will read all points which can take some time.

Now you can load the VPC file in QGIS as any other layer - using QGIS browser, Data sources dialog in QGIS or by doing drag&drop from a file browser. After loading a VPC in QGIS, the 2D canvas will show boundaries of individual files - and as you zoom in, the actual point cloud data will be shown. Here, a VPC loaded together with the overview point cloud:

Zooming in QGIS in 2D map with elevation shading - initially showing just the overview point, later replaced by the actual dense point cloud:

In addition to 2D maps, you can view the VPC in a 3D map windows too:

If the input files for VPCs are not COPC files, QGIS will currently only show their boundaries in 2D and 3D views, but processing algorithms will work fine. It is however possible to use the Create COPC algorithm to batch convert LAS/LAZ files to COPC files, and then load VPC with COPC files.

It is also worth noting that VPCs also work with input data that is not tiled - for example, in some cases the data are distributed as flightlines (with lots of overlaps between files). While this is handled fine by QGIS, for the best performance it is generally recommended to first tile such datasets (using the Tile algorithm) before doing further display and analysis.

Processing Data with Virtual Point Clouds

Now that we have the VPC generated, we can run other processing algorithms. For this example, we are going to convert the ground class of the point cloud to a digital terrain model (DTM) raster. In the QGIS Processing toolbox, search for Export to raster algorithm (in the Point cloud conversion group):

This will use the Z values from the VPC layer and generate a terrain raster based on a user defined resolution. The algorithm will process the tiles in parallel, taking care of edge artefacts (at the edges, it will read data also from the neighbouring tiles). The output of this algorithm will look like this:

The output raster contains holes where there were no points classified as ground. If needed for your use case, you can fill the holes using Fill nodata algorithm from GDAL in the Processing toolbox and create a smooth terrain model for your input Virtual Point Cloud layer:

Virtual point clouds can be used also for any other algorithms in the point cloud processing toolbox. For more information about the newly introduced algorithms, please see our previous blog post.

All of the point cloud algorithms also allow setting filtering extent, so even with a very large VPC, it is possible to run algorithms directly on a small region of interest without having to create temporary point cloud files. Our recommendation is to have input data ready in COPC format, as this format provides more efficient access to data when spatial filtering is used.

Streaming Data from Remote Sources with VPCs

One of the very useful features of VPCs is that they work not only with local files, but they can also reference data hosted on remote HTTP servers. Paired with COPCs, point cloud data can be streamed to QGIS for viewing and/or processing - that means QGIS will only download small portions of data of a virtual point cloud, rather than having to download all data before they could be viewed or analysed.

Using IGN’s lidar data provided as COPC files, we have built a small virtual point cloud ign-chambery.vpc referencing 16 km2 of data (nearly 700 million points). This VPC file can be loaded in QGIS and used for 2D/3D visualisation, elevation profiles and processing, with QGIS handling data requests to the server as necessary. Processing algorithms only take a couple of seconds if the selected area of interest is small (make sure to set the “Cropping extent” parameter of algorithms).

All this greatly simplifies data access to point clouds:

• Data producers can use very simple infrastructure - a server hosting static COPC files together with a single VPC file referencing those COPC files.

• Users can use QGIS to view and process point cloud data as a single map layer, with no need to download large amounts of data, QGIS (and PDAL) taking care of streaming data as needed.

We are very excited about the opportunities that virtual point clouds are bringing to users, especially when combined with COPC format and access from remote servers!

Thanks again to all contributors to our crowdfunding campaign - without their generous support, this work would not have been possible.

Contact us if you would like to add more features in QGIS to handle, analyse or visualise lidar data.

After the addition of support for visualising point clouds in the recent versions of QGIS, the next step was to add the processing tools so users can manage and analyse their data.

There are several 3rd party QGIS plugins (either proprietary or not fully open source) which allow users to interrogate and analyse lidar data. But with our latest work, we have introduced powerful point cloud algorithms to the QGIS Processing framework. All the algorithms are available out of the box in QGIS 3.32, with no need to install plugins.

In this blog post, we summarise the initial point cloud algorithms for QGIS Processing toolbox which will be available in QGIS 3.32 (to be released at the end of June 2023). This work was made possible by the generous donations to our crowdfunding.

Point Cloud algorithms in QGIS

First off a quick look at the new algorithms as shown in the Processing toolbox in three groups:

• Convert formats: this will allow you to convert your point cloud data between LAS and LAZ formats for the time being. Other PDAL supported formats can be added later.
• Export to raster: with this algorithm you can export point cloud to a regularly gridded raster data. It uses inverse distance weighting to assign raster cell values. Raster cells with no nearby points will get “no data” values (these holes may be removed by using “Fill nodata” raster algorithm).

• Export to raster (using triangulation): this allows you to export Z data to a regularly gridded raster by interpolating between the points using triangulation. Note that this can be slower if you are dealing with a large dataset. If your point cloud is dense, you can export your ground points as a raster using the Export to raster algorithm.

• Export to vector: to export point cloud to other vector formats. This is useful to export some of your data for software applications which do not support point cloud data and still use formats such as CSV, Shapefile, DXF.

• Assign projection: assigns a projection to a point cloud layer (if it is wrong or missing)
• Build virtual point cloud (VPC): with this algorithm you can generate a virtual file (based on STAC specification) and load them as a single file in QGIS. There will be a separate blog post detailing this new exciting feature.
• Clip: clip a point cloud layer by a vector polygon layer.

• Create COPC: when you load a non-indexed point cloud layer in QGIS, it will take a while for the application to create the COPC index for your file. With this algorithm, you can create the index for all your files in a batch mode.
• Information: displays information from a point cloud layer:

LAS           1.4
point format  6
count         56736130
scale         0.001 0.001 0.001
offset        431749.999 5440919.999 968.898
extent        431250 5440420 424.266
              432249.999 5441419.999 1513.531
crs           ETRS89 / UTM zone 34N (N-E) (EPSG:3046)  (vertical CRS missing!)
units         horizontal=metre  vertical=unknown

Attributes:
 - X floating 8
 - Y floating 8
 - Z floating 8
 - Intensity unsigned 2
 - ReturnNumber unsigned 1
 - NumberOfReturns unsigned 1
 - ScanDirectionFlag unsigned 1
 - EdgeOfFlightLine unsigned 1
 - Classification unsigned 1
 - ScanAngleRank floating 4
 - UserData unsigned 1
 - PointSourceId unsigned 2
 - GpsTime floating 8
 - ScanChannel unsigned 1
 - ClassFlags unsigned 1

• Merge: join multiple point cloud layers into a single file
• Reproject: reproject the input file to a different coordinate reference system
• Thin (by sampling radius): reduces the number of points within a certain radius

• Thin (by skipping points): reduces the number of points by skipping nearby points
• Tile: this algorithm generates a set of tiles based on the input point cloud layer and tile size
• Boundary: generates a (multi) polygon from your point cloud data. The output file might contain holes depending on the density of your point cloud input data.

• Density: outputs a raster file based on the number of points within each raster cell - useful for quality checking of point cloud datasets

• Filter: it creates a new file based on the filter set as an expression. Note that most of the algorithms support on-the-fly filtering under the Advanced parameters.

Behind the scenes

All the heavy lifting of the point cloud processing is done by PDAL - a state of the art open source library for processing point clouds. PDAL provides a wide range of “readers”, “filters” and “writers” to build complex pipelines to process point clouds.

We have built a new standalone command line tool pdal_wrench on top of PDAL. It addresses two major issues that non-expert users typically face when working with PDAL:

• Ease of use: not everyone finds it easy to manually craft JSON files with pipelines, study manuals of the many stages and read details about file formats involved.
• Parallel execution: PDAL runs pipelines in a single thread, so only one CPU gets to do the work normally and users need to implement their own parallelism if they want to speed up processing.

The command line tool provides a simple set of commands that take care of everything. For example, to export a raster layer with elevations (DEM) with 1 meter resolution:

pdal_wrench to_raster --output=raster.tif --resolution=1 --attribute=Z data.las

The pdal_wrench tool does not depend on QGIS, so it can be easily used separately.

The commands are designed to run in parallel when there are multiple input files or when the input file is in COPC format. Depending on the algorithm, the work gets split spatially into square tiles (1000x1000 map units by default) for parallel processing, or individual files are processed in parallel. With a single ordinary LAS/LAZ file on input, there is currently no parallelism going on.

For commands that are sensitive to edge artifacts (such as export to raster), we take care of processing extra points outside of the extent of each tile (referred to as collar or buffer) to make sure the results are correct as if no tiling would be happening (see Martin Isenburg’s article for more details: https://rapidlasso.com/2015/08/07/use-buffers-when-processing-lidar-in-tiles/).

Future work

The current list of point cloud algorithms already allows users to do plenty of work. But more could be added to the toolbox - algorithms that are already supported by PDAL, but not exposed in QGIS: classification, noise removal, surface reconstruction, clustering, height above ground, colorizing and many more. If you are interested in more point cloud processing algorithms in QGIS, please contact us and we will be happy to add them to future QGIS releases.

QGIS 3.30 was released late last week and we are pleased to announce the new features introduced as a part of our latest crowdfunding campaign to improve 3D, point cloud and elevation data.

Thank you

First and foremost, thanks to the generous support from the community to fund our work. Here is the list of our contributors in no particular order:

IGN (INSTITUT NATIONAL DE L’INFORMATION GEOGRAPHIQUE ET FORESTIERE), National Land Survey of Finland, Danish Agency for Data Supply and Infrastructure, K2 Engineering GmbH, ProScape Consulting, Västra Götalandsregionen, Kristianstads kommun, IGN FI, L’Arrière Guichet, Septima, QWAST-GIS, ATAPEX s.r.o., REDcatch GmbH, F.A.R.M. Facilitazioni Agroecologiche Regionali Mobili, EPYMA TERRITORIO Y MEDIO AMBIENTE, SL, GEO EXPLORATION LTD, Bohannan Huston, Inc., Lidar Guys, Neuchâtel- Service de la géomatique, Wooding Geospatial Solutions, Ville de Vevey, QGIS User Group Switzerland, Ecophylla Consulting, Refactor, Locate Press, Alta ehf, Oester Messtechnik GmbH, RUDAZ+PARTNER AG, BayesMap Solutions LLC, GEOACE, Natalie Gyles, Andreas Neumann, Dougal Munro, Spatial Thoughts, Cicada Systems GIS Consulting, Cori Hermle, Powell Asset Mapping LLC, Darren Farmer, Greg Hall, Ecothought Pty Ltd, Gabriel Diosan, Bhutan QGIS Group, Ultimatum Finesse, Balanced Risk Strategies, Ltd, Concordia University, Burmis Studio Inc., Nicholas Hadaller, Angello Villatoro, Yoichi Kayama, Hennessy Amor Becerra Ayala, Flow Design Limited, BNHR.XYZ, Roberto Moyano, Benjamin Kuster, Goldspot, North River Geographic Systems, Inc, David W. Wormuth, Victor Graphics, Valley Spatial, Stephen Mather, SANTIAGO AURELIO MOTA, Kelly Crowell, Brian Duhan, Paddy Fisher, OSGEO:UK, Christian Gugl, GIP CRAIG - Centre Régional Auvergne-Rhône-Alpes de l’Infromation Géographqiue, Raphael Mabit, Tibor Lieskovský, Kerstin Fohlert, Zhan Li, Bernd Vogelgesang, Marlin Müller, Johannes Bonekamp, Stefan Giese, Fabian Faßnacht, QGIS Sweden user group, Falo, DAVID GARCIA HERNANDEZ, Lint Data and Geospatial, Cliff Sin Wai Lau, Grzegorz Sapijaszko, Łukasz Rapa, Alessandro Pintucci and Maarten Pronk.

Our gratitude also goes to those who want to remain anonymous.

Global map shading

To see this feature in action, you can open QGIS project properties and under Terrain, there should be an option for Global Map Shading. You will need to first add a raster as your DEM under the Terrain section.

Elevations of all these layers are combined and the considered elevation is chosen depending on one of two methods:

1. the highest elevation between raster, mesh or point cloud layers will be selected.
2. elevation will be selected based on the order of layers in the layer panel.

Depending on the context and the use of the map the user can choose the more appropriate method.

For now, the shading methods implemented are the Eye Dome Lighting and the hill-shade. More methods could be added in the future - such as ambient occlusion.

The user can choose the elevation shading settings in a specific UI widget that can be found in two places:

1. under the Project Properties, within the same tab of the project elevation settings.
2. under the Styling Panel, a new tab is added for quick access to the user.

Profile elevation within print composer

This work was carried out by our collaborator North Road. The profile tool can embed elevation profiles within print layouts. It is possible to add beautifully styled profiles in your print outputs.

To use elevation profiles in print layouts, simply click the Add Elevation Profile button in the toolbar, and then to initialize it, copy profile configuration from an existing elevation profile from QGIS main window using the “Copy From Profile” button.

3D navigation improvements

With the new improvements, you can:

1. move camera vertically using ctrl+shift+left mouse button
2. keep zooming with the wheel while moving the mouse
3. continue right mouse button zooming when pointer exits the viewport

Improve zoom-extents in 3D

The terrain’s and point cloud layers’ elevation range are taken into account so that the camera is not positioned below the scene’s contents, which was the case when using the terrain’s vertical scale setting to exaggerate the elevation differences.

Limit 3D scenes’ 2D extent

3D Views can now be limited to a specific 2D extent. The terrain is clipped and no 3D features beyond that extent are loaded, making it easy to render specific areas of big QGIS projects. The project’s 2D extent is used by default which can then be adjusted in each 3D view separately using the new General tab in 3D configuration.

Future updates

There will be more features planned for QGIS 3.32. We are finalising the Processing framework for point cloud data and it should be available on QGIS master in coming weeks.

Please do not hesitate to contact us if you have any suggestions to improve QGIS 3D and point cloud support.

Listen to the latest developments in point clouds and QGIS from Martin Dobias: MapScaping podcast.

Martin Dobias, our CTO and the lead developer of 3D and point clouds integration in QGIS sat down with Daniel O’Donohue from Mapscaping to talk about point clouds and QGIS.

Martin discusses his early involvment with QGIS back in 2005 and how he started his journey to become a QGIS developer.

With the recent changes to the Mergin Maps plugin for QGIS, you can visualise the local changes before synchronising your data.

Have you ever been in the situation when, after making a lot of changes in your Mergin Maps project, you hesitate to press Sync button because you are not sure that all required changes are made or afraid that some unwanted edits were introduced? Or maybe you need to review the work done and see what actually have changed between two versions? If the answer to any of these questions is “yes” then you will like the changes visualisation functionality we introduced in the 2022.4 version of the Mergin Maps plugin for QGIS.

Changes visualisation functionality comes handy in two use-cases: revising local changes made in the Mergin Maps project before syncing them with the server and getting a list of changes between two versions of the project. Let’s take a closer look at this feature.

Local changes visualisation

While working with Mergin Maps project, the user can at any time revise their current changes made locally. First, make sure that all your layer’s edits are saved (committed) as currently viewing of the unsaved changes is not supported. Then right-click on any vector layer and select “Show Local Changes” entry in the context menu.

Accessing local changes from context menu

This will open the Changes Viewer dialog. Each vector layer with local changes has its own tab in the Changes Viewer dialog, the name of the tab matches the layer name and also contains information about the number of changes in this specific layer. Local changes are shown on the map and in the tabular form, to distinguish different types of edits a following color codes are used: inserts (new features) are green, edits orange and deletions red. It is possible to enlarge or reduce the size of the map and table by dragging the splitter between them, splitter position is applied to all tabs and will be saved and reused on the further dialog calls.

Features added, deleted and modified in map and tabular views

Map canvas in the Changes Viewer dialog supports basic operations like panning as well as zooming in and out. By default, all project layers are shown on the map to provide better context, but it is possible to toggle their visibility by unchecking the “Toggle Project Layers” button in the toolbar above the map. When this button is unchecked, only changes from the current vector layer are shown.

If, after some panning/zooming, you need to return to the extent where all changes are visible — press “Zoom Full” button. Also, it is possible to select a specific feature(s) in the table below map and zoom to them by clicking the “Zoom To Selection” button. Finally, changes can be added as a new memory layer to the current project. To do so, click “Add to project” button and choose one of the options: add changes from the current layer or add all changes from all layers. For each changed layer, a new memory layer will be added to the current project. These changes layers will preserve the same color coding for features and attribute table as used in the Changes Viewer dialog. Please note, that these layers should be manually removed from the project before the sync, unless it is your intention to make them a part of your Mergin Maps project. Another way to revise local changes is to open Changes Viewer from the Project Status dialog by clicking “View Changes” button.

Mergin Maps Processing tools

Sometimes one may want to export local changes as a vector layer and save that file for further usage. Of course, this can be done with the help of Changes Viewer dialog, but it is time-consuming, especially when the Mergin Maps project has many layers or if there is a need to check local changes in several projects. To cover this use-case, we also provide “Extract local changes” tool. This tool is a part of the Mergin Maps QGIS plugin and can be found under the “Mergin Maps” group in the Processing Toolbox.

Mergin Maps Processing tools to create changeset

In the tool dialog you need to specify a directory with your Mergin Maps project, select a layer of interest either choosing from available layer or selecting a GeoPackage file in the project directory and layer in this file.

Processing tool to extract local changes

An output layer containing local changes will be created as a temporary or regular layer and added to the current project. This layer will have the same styling (both for features and attribute table) as the layers produced by Changes Viewer dialog.

Result of the local change processing tool

The “Create diff” tool comes handy when you need to revise the changes between two versions of the layer in the Mergin Maps project. This tool is also a part of the Mergin Maps QGIS plugin, and it is implemented as a Processing algorithm. The “Create diff” tool can be found under the “Mergin Maps” group in the Processing Toolbox.

The tool dialog is quite similar to the “Extract local changes” tool dialog. Fill in input values: directory of your Mergin Maps project, layer of interest, start and end version numbers. Finally, specify location of the output vector layer or leave the field empty if you want it as a temporary layer in your current project. After clicking “Run” the tool will query the server for information and generate a vector layer containing all changes made between specified layer versions. For example, if some field value was changed in one version and then the same field was changed again in another version, then only the last change will be shown in the output changes file.

This feature is an another step in our ongoing efforts to create an easy-to-use tool for collaborative data collection and data management. If you need help or want to share your experience with Mergin Maps QGIS plugin, please join us in the community chatroom, and we will be happy to hear your thoughts.

Mergin Maps and QGIS used for municipal waste composition survey in the Czech Republic.

Global Environmental Threat of Municipal Waste

The dumping of municipal waste is a global threat to our environment and all life forms.

Currently, there is a distinct trend of less landfilling, as countries move steadily towards alternative ways of recycling and incineration, where material use is not possible.

Dr Martin Pavlas, Associate Professor at the Institute of Process Engineering in the Faculty of Mechanical Engineering at Brno University of Technology in the Czech Republic, is doing important research as part of an EU project regarding municipal waste sampling.

Bins to be sampled (Photo: M. Pavlas)

With the aid of Mergin Maps and QGIS, he is carrying out an extensive municipal waste composition survey in the Czech Republic. Together with Peter Petrik of Lutra Consulting, a unique GIS-based tool was developed for the waste sampling. This includes a prototype mobile application based on Mergin Maps for waste sampling in the field.

We talked about Mergin Maps in the MapScaping podcast: QGIS Offline And In The Field

Peter Petrik was a guest in the episode of QGIS Offline And In The Field. He talked with Daniel O’Donohue about collection of spatial data in the field.

Mergin Maps is a field data collection app based on QGIS. It makes field work easy with its simple interface and cloud-based sync. Available on Android, iOS and Windows.