Posts by Lutra Consulting

QGIS 3.18 is finally here. This will be the first release of QGIS with native support for point cloud data. This work was made possible by generous contributions from the community.

(Data from UGKK SR, made by Tibor Lieskovsky)

Contributors

When we announced the campaign in August 2020, the response was overwhelming and within weeks, we managed to reach and then exceed the amount required.

Below is the list of contributors in no particular order:

Mapfly, Ujaval Gandhi from Spatial Thoughts, National Land Survey of Finland, Daniel Löwenborg, BNHR, Imapct GIS, Andreas Neumann, Kanton Solothurn, Switzerland, City of Vevey, Mapping Automation, Service de la géomatique du canton de Neuchâtel, Hans van der Kwast, 3DGeoCloud, Rudaz+Partner AG, Leonard Gouzin, TileDB, Inc.

We wish to also thank the many anonymous contributors who do not appear in this list.

Note: if you have contributed to the campaign but your name does not appear here, you may not have selected the option to be listed in the campaign form. Please contact us if you’d like to be listed.

In addition to financial contributions, we’d like to extend our gratitude to all those who helped spread the word, helped with the testing and provided feedback and sample data.

New features

This is a brief summary of new features from our joint work with North Road and Hobu:

• Introduction of a new map layer type: a point cloud layer
• Load LAS or LAZ files (either by drag’n’drop or by opening files in Data Sources Manager)
• Load EPT datasets by pointing QGIS to their ept.json file (currently only supporting datasets on the local drive)
• Support for rendering point cloud data in 2D and 3D map views
• Apply various rendering styles in 2D and 3D:
  • “Attribute by Ramp” - draw data based on a single attribute and a color ramp (similar to “Graduated” styling for vector layers)
  • “RGB” - draw data using colors assigned to the points (combining red/green/blue attributes)
  • “Classification” - draw data using different colors for different classes (ground, buildings, vegetation, …), also allowing display of only desired classes
  • “Extent only” (2D only) - draw only bounding box of the point cloud
  • “Single color” (3D only) - draw all points with a single color
• Set size and shape of points
• Manually adjust scaling and offset of elevation (Z values) - if needed to match with elevation of other data
• Point cloud layer properties dialog to see metadata of the point clouds
• Identify tool supports point cloud layers and shows all attributes of picked points
• 2D and 3D views only render a subset of the point cloud for the best performance for the given view (for geeks - this is thanks to indexing to octree data structure, using EPT format written by untwine tool packaged with QGIS)
• Optimize the quality and performance of the 3D view using point budget configuration, which limits maximum amount of point rendered at any time (set to 1 million by default)
• Enable eye-dome lighting in 3D views for much better depth perception of point clouds
• New “Walk mode” camera navigation in 3D views - there is now a switch between the original “Terrain based” navigation mode and the new mode, which allow easier navigation through point cloud data

Please note that as this is the initial release (with over 10 thousand lines of new code related to point clouds), there may be still some rough edges here and there, or some data may not load or display correctly. In case you encounter any issues with the new functionality, please let us know - do not hesitate to create a new QGIS issue

(Data from Helsinki City)

Future work

This has been the start of a larger effort to bring full support for point cloud data into QGIS. We, in collaboration with North Road and Hobu are developing requirements for integrating point cloud data processing and analysis, more data formats, better visualisation, profile tools etc. in future releases of QGIS.

If you’re interested in helping shape those requirements or funding such features, please contact us at info@lutraconsulting.co.uk.

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In the past months, we have been busy adding support for point cloud data in QGIS (3.18). Read more on how to install and test this feature under MS Windows.

Installation

To be able to use this feature, you need extra packages and also the latest OSGeo4W installer.

Update

Since the original post, Jürgen Fischer has created a stand-alone MSI installer. You can download the new installer from the QGIS website. The installer is only for 64-bit platforms and does not support MS Windows 7.

Note that there have been several regressions and bugs with the first release of QGIS 3.18.0. The issues are being addressed and soon there will be an updated version available. The above link is only for those who are eager to test the point cloud data in QGIS.

Installation

To be able to use this feature, you need extra packages and also the latest OSGeo4W installer.

Note: This is a completely revamped and different packaging system than the current OSGeo4W installer. To avoid any clash with your current installation, it is recommended to use different paths for temporary download files and installation of the new packages. The new packaging only supports 64-bit platform.

1- Download and run the NEW OSGeo4W installer

2- Select the Advanced install and pick qgis-dev from the list of packages

Special thanks to Jürgen Fischer for his hard work on preparing the new packages for Windows.

Testing

Once installation is completed, try to run QGIS from the installation path (e.g. C:\OSGeo4W64\bin\qgis-dev.bat). You should be able to load LAS\LAZ file to your map from the Browser panel or the Data Source Manager.

The point cloud data can be visualised in 2D and 3D map canvas.

Please test and let us know if you encounter any problems when loading, viewing or styling point cloud data. The best way to do that is to create a new issue on GitHub: https://github.com/qgis/QGIS/issues

QGIS 3.18 will be released later this week (February 19), so grab your copy of QGIS today and give it a try, so that we can fix any remaining issues before the release!

Troubleshooting

Problem: I am unable to add any LAS/LAZ point cloud file

Solution: Ensure you have used the correct installer linked above. Development builds of QGIS in the ordinary OSGeo4W installer DO NOT include support for point clouds.

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The case study presents the implementation of the QGIS FLO-2D Plugin project (5 minute read)

Introduction

FLO-2D is one of the most widely used commercially available flood models. FLO-2D is capable of simulating urban flooding in high resolution including storm drain systems.

In 2016 the FLO-2D team invited us to develop a set of tools for optimising the flood model build process in QGIS. The resulting plugin allows hydraulic models to be built quickly by leveraging the wide range of tools available in the QGIS ecosystem.

The plugin can be downloaded by following the guidance in the plugin documentation

As the use of open source GIS grows within the water engineering sector, Lutra Consulting develops and maintains MDAL which makes the visualisation and post-processing of time-varying numerical model results possible in QGIS.

Contact us at info@lutraconsulting.co.uk if you’d like to discuss the benefits of integrating your flood modelling software more tightly with QGIS.

Overview of Previous GUI tools: FLO-2D GDS

FLO-2D’s focus on urban modeling requires large datasets that may include several million grid cells. Each cell has between 4 and 10 attributes so datasets can often be in the range of several gigabytes.

Previously, Grid Developer System (GDS) was used to pre-process the spatial data used by FLO-2D. Being a 32-bit application, the GDS was only able to load up to 4 gigabytes of data and its programming framework was also no longer maintained by Microsoft. Modellers could only be built on Windows PCs.

All development and maintenance of the GDS was carried out by the FLO-2D team.

Behind the Implementation

Guiding principles behind the new QGIS Flo-2D plugin

We assessed the tools used at the time and in close collaboration with the FLO-2D team, optimised the workflow from the users’ point of view with emphasis on speed and simplicity.

To keep things simple for the users, we aimed to provide a level of abstraction so users would no longer need to be involved with the internal structure of FLO-2D solver input files. They could instead focus on real-world aspects affecting their models.

Together with the FLO-2D team we designed a solution that was based on 3 core ideas:

• Modellers will use native QGIS tools and point/line/polygon layers to define real-world objects (e.g. domain, boundary conditions, levees, …)
• These real-world objects will be converted automatically into the data structures required by the FLO-2D solver (although expert users can still modify these data structures if they so wish).
• Additional productivity tools will be provided to allow users to speed-up time consuming tasks

Features of the plugin

Some of the key features of the solution are listed here:

• Easy creation and handling of new models (all model data sits in a single GeoPackage file)
• User friendly and intuitive digitizing and manipulation of model components (through simple GIS layers and dedicated tools) including:
  • 1D domain
  • Boundary and initial conditions
  • 1D channels and cross-sections
  • Levees
  • Rainfall
  • Infiltration areas
  • Storm drains
• Various options for obtaining grid data such as elevation, roughness, reduction factors etc. from different sources (raster layers, external vector layers etc.)
• Plotting profiles and editing time series data
• Automated tools for schematizing input layers into the GDS format
• Importing basic data from HEC-RAS models
• Import / export functionality between GeoPackage and GDS format (*.DAT files)
• Running external FLO-2D tools (FLO-2D Pro engine etc.) directly from QGIS

Benefits for the FLO-2D developers:

Benefits for the developers of FLO-2D include:

• Reduced development and maintenance costs, since much of the heavy lifting of the FLO-2D plugin is done by QGIS itself
• By being part of the QGIS ecosystem, gaining opportunities to approach QGIS users in the flood risk industry to use FLO-2D software
• The FLO-2D plugin is developed on GitHub, allowing the latest development technologies such as continuous integration, automatic testing and issue tracking to be used
• Ability to solve upstream bugs in QGIS or MDAL due to the open-source nature of the projects

Benefits for FLO-2D users:

Some of the benefits realised by FLO-2D customers include:

• Being able to work with their FLO-2D models using open source GIS on all major operating systems
• A full GIS application to support their data pre-processing
• Logical and intuitive workflows
• FLO-2D results can now be visualised and post-processed natively in QGIS via mesh layer
• Ability to use all native QGIS support and development channels in addition to FLO-2D support
• Integration of internal workflows with powerful native QGIS features including projection support, GDAL/OGR integrations, background maps support (e.g. vector tiles), printed flood maps, etc.
• Ability analyze results via QGIS’ Crayfish plugin and produce graphs and outputs

The plugin in action!

Further Reading

• FLO-2D webpage
• MDAL and Mesh Layer

Do you have any questions or would like to see demo of QGIS Mesh Layer? Contact us at info@lutraconsulting.co.uk or schedule a demo call calendly.com/saber-razmjooei/15min

Key words

QGIS, plugin, python, migration, optimised, speed up, fast, hydraulic modelling, water, 2D, open-source, cost reduction, software development

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Preparing reports with lots of survey photos takes time - this plugin automates the process.

Nowadays, it’s really easy to take georeferenced photos on site visits. Tools like Input can capture photos, descriptions and location information and bringing it all into GIS is straight-forward. However.. I recently discovered that people still spend significant amounts of time organising their photos into reports outside of GIS. This small plugin automates the process of getting the photos and their descriptions into Microsoft Word.

For this blog post I knocked-up a simple survey project based on the Field notes Mergin template. Points can have a photo, title and description amongst other fields. No comments on my gardening skills please!

When I sync the data back to QGIS, the attribute table looks something like this:

We’ll now use the HTML Table Exporter plugin to export the layer as an HTML table (this will let us get it into Word).

First install the plugin.

It’s an experimental plugin so you’ll first need to check the Show also experimental plugins option under Plugins > Manage and Install Plugins… > Settings.

When you have installed the plugin, open it using this button or via Plugins > HTML Table Exporter > Export table as HTML.

You should now see this:

Set Table to the layer you want to export. The image scaling option is described later, leave it at a small setting for the time being. Click OK to export and tell the plugin where to save the HTML file.

Now the data should be out of QGIS.. the next steps are in.. MS Word :o

Right click on the exported html file and open it with Microsoft Word:

Let’s see what it looks like:

OK.. we’re getting somewhere! At this point you’ll want to:

• Enable Print Layout mode in Word so you can see what the printed page will look like
• Set the page orientation as desired (I chose landscape in the image above)
• Experiment with image rotation if required (Right click photo > Size and position > Rotation)

If playing with rotation, just focus on rotating a single image, we’ll batch rotate later as required. For now you want to get a feel of whether you want to adjust the scale factor in QGIS (to make the images smaller or larger) to save you having to resize them individually in Word.

I’ve decided to increase the scale factor from 10% to 15% so will now re-export.

Beware that Word has an exclusive lock on the html file when it’s open so you need to close it in Word before you can export it again from QGIS.

I settled for 10% in the end so I could get multiple images on each page in portrait mode. After removing the columns I didn’t want, the table in Word now looks like this:

To rapidly rotate images by 90 degrees, rotate the first one using Right click on photo > Size and position > Rotation then select subsequent photos and press the F4 key. This method is described in more detail here as well as other Word batch image rotation methods.

My document is almost finished. There are just a few small issues to iron out. Currently, the images are referenced by the Word document, not embedded. This means if I email the document to someone, the images will be missing. Let’s fix that by embedding the images in the word document.

First save the document as a Word document in Word’s native format (e.g. *.docx).

Next, locate the Edit Links to Files option:

Select all the linked images (the shift and arrow keys help here) and check the Save picture in document option and click OK:

Save the document, your photos should now be embedded within the document.

If you find your word document gets huge, you can use the method here to quickly batch compress all images in the document.

Input is a free and open source field data collection and mobile GIS app based on QGIS.

If this guide saved you some time and you feel like doing something awesome for us, a review of Input on the Apple App Store or Google Play Store would be really well appreciated.

QGIS Mesh Layer now support DHI dfsu and HECRAS 6.0 format.

The releases of QGIS 3.16 LTR/QGIS 3.18, MDAL 0.8.0 and Crayfish 3.5.0 are planned for 19 February 2021. We are delighted to present the following improvements for the upcoming releases:

• In-memory mesh datasets with persistence
• Multi identify tool for mesh
• Virtual dataset groups for mesh layer
• 3D Mesh Layer Bug Fixes and speed optimizations
• DHI’s dfsu format support (QGIS 3.18 only)
• HECRAS 6.0 format support

If you’d like try the latest features, you can always install QGIS nightlies/master, which comes with all the latest features described in this blog post.

If you want to learn more about Mesh Layer in QGIS, read more here…

Mesh Calculator and Layer Improvements

The Virtual dataset groups for mesh layer and In-memory mesh datasets with persistence improvements greatly improves the workflows when using the Mesh Calculator in QGIS. Users can store the intermediate results into virtual layers that are recalculated on the fly (similarly to QGIS expressions for vector layers). The layers can be later persisted to any supported MDAL formats with write capabilities.

Multi identify tool for mesh feature allows to browse the temporal mesh data in more intuitive way and includes the extra information about the Mesh datasets loaded.

These features were sponsored by Artelia Group.

DHI’s dfsu format support

MDAL 0.8.0 supports loading of the external drivers. A first driver, available on Windows QGIS 3.18 only, is popular DFSU format by DHI, which is used to store MIKE 21 output results.

You can see how to configure and use QGIS to work with DFSU format on the DHI’s YouTube channel

Special thanks to the sponsor DHI this feature.

QGIS

We have added the following new features to QGIS to convert between mesh and vector/raster:

• TIN Mesh creation
• Ported most of the processing algorithms from Crayfish to QGIS core
• 3D rendering improvements
• Many Mesh Layer bugfixes

MDAL

• Support for external drivers.
• Fixed HECRAS vector datasets support
• Packaging in conda

Crayfish

• Fixed SAGA flow direction support
• Fixed FFMPEG download link
• Ported most of the processing algorithms from Crayfish to QGIS core

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One of the challenges of data collection projects is consolidating all the data a central database, such as Postgres/PostGIS. Using PostGIS as a live survey layer is not usually recommended:

• Security: exposing your database to the wider internet
• Access and connectivity: internet connection is not always guaranteed for field survey projects

A more cumbersome way around this, is to convert your tables from PostGIS to a file based GIS layer (e.g. GeoPackage) and take the files with you to the field. This will create a new problem: keeping all the tables (from multiple surveyors) and the PostGIS table up-to-date.

During a survey to assess water access for villages in Limpopo province, South Africa, our friends at Kartoza have commissioned us to extend the Mergin service to support PostGIS. The Mergin service already supports file-based data synchronisation. The aim was to bridge the gap between the Mergin service and PostGIS so that the changes from Mergin immediately appear on PostGIS and vice versa.

To facilitate that, we further developed the Geodiff library to support Postgres driver. In addition, we developed mergin-db-sync tool to sync the tables from Postgres database with the Mergin service. The mergin-db-sync tool runs as a service (daemon) that keeps an eye on a particular Mergin project, and if there is a new version of the project, it will fetch the most recent changes and apply them to database tables in PostgreSQL. It works also the other way around at the same time: it looks for changes in the configured PostgreSQL schema and upload them in a new version of the Mergin project if any changes were detected. This service can be easily started on the local Intranet (where the PostgreSQL database is run) and therefore it does not need any adjustments to the firewall to allow access to the local database from public Internet.

The above diagram details how Postgres/PostGIS synchronisation works with the Mergin service via the DB-Sync tool.

• Tables 1 and 2 from the Postgres/PostGIS server are set up to work with the Mergin service
• DB-Sync tool runs on a server on a regular interval to sync with the Mergin service
• An offline version of Tables 1 and 2 are provided within the QGIS survey project on Mergin
• Several surveyors download the project and add their data mostly while offline. The data are then synced with the Mergin.

From the surveyors’ point of view, the extra set up to sync with the Postgres/PostGIS does not affect their workflow. In fact, mergin-db-sync tool acts as another client syncing data to the Mergin project, therefore it is possible to see all the changes in the project log originated from mergin-db-sync tool.

The tool is available on GitHub with a permissive open source license (MIT). At this point it supports PostgreSQL, but the mechanism is fairly generic and support for other database engines may be added in the future without great effort. All the heavy lifting is done by the Geodiff library which has been significantly enhanced during the development of mergin-db-sync tool.

To try the tool, please follow the instructions on the project’s readme on GitHub. The easiest way is to use it in a Docker container.

If you have any issues or feedback to enhance the tool, you can file a ticket on the project repo.

If you’d like to set up DB-Sync tool with your Mergin survey projects, you can contact us on info@lutraconsulting.co.uk

Raster selection tools and editing with QGIS expressions.

What’s new?

In the previous version Serval allowed for setting a constant value to a single raster cell at a time. The latest version brings in new raster cells selection tools and new ways of manipulating cells value:

• QGIS expressions and
• 3x3 low-pass filter.

Multi-bands rasters are fully supported - each band can be edited separately. Serval is meant to provide a convenient way to modify selected parts of a raster layer. It is not optimized to process entire images - use Raster Calculator for that.

Raster selection tools

In order to modify raster cells values users need to select them first with a line or polygon selection tool. The line selection tool has a configurable width and unit. Selections can also be easily imported/exported from/to a vector map layer.

Multiple selections are allowed and users can add or remove from existing selection using CTRL and SHIFT keys modifiers, respectively.

Modifying rasters with QGIS expressions

In addition to setting a constant value, new cells value might be evaluated using QGIS expressions. Users can build their expressions using cells’ row and column variables, or x and y of cell center point.

In the Serval group of the builder users will find several expression functions for:

• interpolating raster value from a mesh layer,
• averaging attribute values from vector layer features intersecting the cell,
• getting the nearest vector layer feature attribute,
• interpolating z-value from the nearest 3D linestring feature.

Interpolation on a mesh layer

With the recent QGIS additions it is very easy to create mesh layers. Users can use a mesh layer for raster cell value interpolation. For example, interpolate_from_mesh() function will identify mesh dataset value and return it as a new raster value, optionally only when the value is higher than existing cell value. It is a convenient way for finding an embankment shape on a raster DTM - see pictures below.

Interpolation on 3D linestrings

Another useful function finds the nearest point on a 3D line feature and interpolate its z-value. It could be used for fine-tuning terrain elevation from a 3D profile line.

For example, if the existing embankment crown needs to be raised by an amount at one of its ends, a user could create a profile line, adjust the vertex elevation in the Vertex Editor and use it for the interpolation.

Creating custom expression functions

In the Expression builder users can define their own functions using other types of vector and raster layers.

Applying 3x3 low-pass filter

For eliminating peak values or local cell values averaging, users can use 3x3 low-pass filter.

For more details and examples, refer to the plugin’s Manual.

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In this year Google Summer of Code (GSoC), multiple new features were added to QGIS 3D. Belgacem Nedjima as the student with Martin Dobias and Peter Petrik as the mentors have implemented the following features into the 3D viewer of QGIS:

Feature 1 : Textures support

Now it is possible to add textures to vector layer data (buildings) in the 3D viewer: To do that you need to go the vector layer properties and select Realistic Textured (Phong) shading mode and select the image to be displayed on the surfaces by providing an image file to the diffuse texture field. You can adjust the scale of the image and its rotation from the texture scale and texture fields respectively. There is a workaround to seperate roofs from walls using rule based rendering by adding 2 seperate rules and selecting which facet (roof or a wall) to display and select which rendered facade to be used and the shading setting for each rule. Results for seperating roofs and walls

Feature 2 : Directional lights

QGIS 3.16 adds support for directional lights that you can think of as the sun. The default light in QGIS 3.16 is now directional which makes the lighting of the whole scene better. To use directional lights, open the 3D configuration dialog and from the lights tab you can configure how many lights you use. If you select a directional light, you will see the new improved way to provide the direction of the light (Thanks to Vincent Cloarec for the UI improvements).

Feature 3 : Scene export as .obj file

In the 3D viewer there is a new tool to export the scene as a .obj file and visualize or edit it in other software like Blender or MeshLab. To export the scene, from the 3D viewer select the 3D object icon and you will see the following dialog: You can select the name of the .obj file and where to save it (you need to select a folder because you will have multiple files if you export textures or color informations). You can specify the resolution of the terrain in case you’re using a DEM as well as the resolution of the exported textures. Here is a view of what an exported scene that has multiple rendering rules looks like in MeshLab:

Feature 4 : Skybox rendering

You can add a skybox to your scene from the 3D configuration dialog and selecting from the 2 skybox formats provided:

• Panoramic Texture: One 360 image is used to generate the skybox
• Distinct Faces: 6 different faces describing the cube map of the skybox The result of applying a skybox to the scene:

Feature 5 : Shadow rendering (beta)

Althought it might not work for everyone, shadow rendering was added as a technical preview in QGIS 3.16. To try shadows, you need to open the 3D configuration dialog and enable shadows from the Shadows tab. You can experiment with the shadow rendering parameters if you want to decrease the visual artifacts and make the shadows look better in the scene. Here is how the shadows look like in our scene:

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QGIS 3.16 on macOS coming with Proj6, GDAL3 and major fixes! macOS packaging mechanism is completely reworked for QGIS 3.16. This will bring QGIS on macOS on par with other platforms that already benefit from the new versions of PROJ and GDAL libraries - especially the greatly improved reprojection support.

QGIS nightly and QGIS 3.16

If you want to try the new packages, download and install the QGIS from nightly builds. There are still few weeks before QGIS 3.16 release for testing and bug-fixing, so make sure you report all your issues before the 23rd October 2020. Multiple packages now can be installed side-by-side, just rename or move the installed QGIS.app!

The earlier packages were based on Homebrew, but we didn’t have control over the versions of dependencies. We switched to the new system where we have full control, which is important for good quality releases.

The package/installer is not yet notarized by Apple, so you need to right-click on the QGIS.app icon and open it to overcome the security control of your macOS (only for 10.15+).

If you want to join the effort in testing and/or development of macOS packages, please drop me a mail on peter.petrik@lutraconsulting.co.uk We have a dedicated Slack channel to discuss the maintenance of the macOS packages.

What is in the all-in-one bundle?

The goal is to have all advanced functionality of QGIS prepared and ready to use after simple one-click installation.

QGIS and utilities

QGIS Desktop, of course, but also

• QGIS server (try with /Applications/QGIS.app/Contents/MacOS/bin/qgis_mapserver)
• QGIS process (try with /Applications/QGIS.app/Contents/MacOS/bin/qgis_process)
• QtDesigner for custom forms (/Applications/QGIS.app/Contents/MacOS/bin/designer)
• ogr2ogr and various other gdal utilities

FOSS4G libraries

• Geos 3.8.1
• Proj 6.3.2
• GDAL 3.1.2
• GRASS 7.8.3
• SAGA 7.3.0

Python 3.7

with pip, so you can install the missing packages with command /Applications/QGIS.app/Contents/MacOS/bin/pip3 install <your package>

but, many packages are already preinstalled for you!

• pipenv
• requests
• plotly
• matplotlib
• scipy
• numpy
• shapely
• geopandas
• gdal
• h5py
• pyproj
• pillow

QGIS Processing

• GRASS processing tools
• GDAL processing tools
• SAGA processing tools
• OTB processing tools (needs external installation of OTB)

Data Providers

All basic providers

• GeoPackage
• Spatialite
• DB2
• WCS/WFS/OWS/WMS/WMTS
• Vector Tiles
• XYZ Tiles
• OGR/GDAL
• PostgreSQL
• MDAL

But also:

• ECW
• MrSID
• MSSQL
• OracleDB

Acknowledgments

In Spring 2020, we prototyped the building of FOSS macOS libraries in completely controlled environment. Few weeks ago we have successfully finished the QGIS 2020 Grant “QGIS macOS Package Improvements”. This wouldn’t be possible without support from QGIS.org and its sponsors. And without proper testing and reporting of issues from our macOS power-users.

QGIS for iOS

Do you want to see your QGIS projects and data from your iPhone and iPad? Check InputApp

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We are delighted to announce that in collaboration with North Road and Hobu, we are running a crowdfunding campaign to implement native support for point cloud data in QGIS.

With the proposed changes, you will be able to load, style and visualise your point cloud data in QGIS in 2D and 3D map views.

The work will be carried out by the trusted and highly skilled developers across PDAL and QGIS community (Lutra Consulting, North Road and Hobu) who have been at the forefront of some of the exciting features in Open Source projects.

If you or your organisation are point cloud data users, this is your chance to bring native support for your data in QGIS. With this work, you will be able to overlay your point cloud data to your other data (vector/raster). The addition of native support for point cloud data in QGIS will pave the way to support analytical tools for point cloud data in future.

The target amount is 49,000 € and the campaign will be active until 15 October 2020.

Please have a look at the dedicated page point cloud data support in QGIS for further details and help us spread the word!

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