The Challenge

Stretching 600 kilometers along Germany’s western border lies a vast network of concrete bunkers and fortifications built in the 1930s and 40s. Known as the Siegfried Line (or Westwall in Germany), this defensive system comprises approximately 20,000 distinct structures, half of which still exist in some form today.

For decades after WWII, these structures were systematically destroyed or buried. But attitudes have changed. Today, they’re protected as historical monuments, and volunteers working with German federal monument services are documenting, monitoring, and securing them before they deteriorate further or pose safety risks.

Patrice Wijnands, a geomaticist and volunteer coordinator, has been mapping these fortifications for 30 years. Five years ago, he discovered QField, and it transformed how this massive conservation effort operates.

The fortifications present unique challenges. Many were filled with sand in the decades following the war. Now, that sand is settling and draining away, creating new gaps and hazards.

“The sands with which these bunkers had been filled up is now filling in and it opens new gaps, new holes in the surface. That is especially the thing we are tackling because we not only map these objects, we also get back to them every few years.”

The scale is immense: tracking safety risks, determining protective measures, and monitoring changes across a zone 600 kilometers long and up to 30 kilometers wide. Some structures near populated areas require fencing. Others in remote woodland need only warning signs. The key is knowing which is which, and tracking how conditions evolve.

From Paper Maps to Digital Collaboration

For 15 years, volunteers mapped using paper maps, each person working on their own island. Patrice would receive photocopied maps and Excel spreadsheets, spending hours trying to reconcile different symbologies.

“What does that mean when they made a cross on the map? How does that fit with their Excel sheet that they maybe sent me in 2005?”

Patrice began using specialized GIS software in 2010, but found it difficult to share projects. In 2017, he started experimenting with QGIS. Then in 2019, he discovered QField.

“I started using QField in 2019 and I felt that this was the state of the art after the tools I used before.”

Scaling Up with QFieldCloud

Within six months, Patrice began distributing QField projects to other volunteers. But as the network grew—especially during COVID-19—manually distributing projects became unsustainable. Then QFieldCloud arrived, solving the scalability problem completely.

“I just create these projects. I put them onto the cloud. People can gather them. They go into the field. The only thing I need to explain to them is how to map, what data are to be collected. They learn that within a few minutes. They upload these data and synchronize again with the QField cloud. And I can see these data in the next minutes again already here on my desktop.”

The project by numbers:

• 600 kilometers: Length of the fortification zone
• 20,000 objects: Total fortifications mapped today
• Scalable to 100+ volunteers: Thanks to QFieldCloud

Keeping It Simple

Volunteers work with point data, adding observations to a monitoring layer rather than editing the base map. This eliminates GPS accuracy issues—critical when working under forest canopy where positioning might be off by 50 meters.

“Every person visiting an object adds a point. That means if next year somebody else is getting there, he adds a new point.They only add their observation and the interpretation is something for me and for the people post-processing this data later on.”

The system captures photographs, condition assessments, and observations about changes since the last visit. Because volunteers often work in areas with poor mobile coverage, Patrice includes offline maps and advises volunteers to synchronize at home.

Learning QField: Minutes, Not Months

New volunteers become productive quickly. Patrice typically provides initial training through a single online or face-to-face session, and volunteers are mapping independently within weeks.

“I have seen people going into this without even having a GIS system before and they work with this after a few weeks as if they have never done anything else before. It does not need some software in which you need to spend a lot of time to learn it. That’s my job. And all the people outside in the field, they don’t need to understand that in that depth.”

This ease of adoption means Patrice can focus volunteers on what matters: understanding the history and significance of what they’re documenting.

Data That Serves Multiple Purposes

The database serves various stakeholders with different needs. Some structures can be made public for educational purposes. Others must remain confidential due to safety concerns, property rights, or ecological considerations.

Federal monument agencies receive data they need. Property owners get information about structures on their land. Ecologists access data about habitat. And the public gains access to appropriate historical information.

The system is also adaptable. Patrice has successfully tested the data model on other historical fortifications, demonstrating the approach could be applied to different types of monuments.

The Difference Open Source Makes

For Patrice, the open-source nature of QGIS and QField proved essential. Unlike proprietary alternatives he’d used previously, these tools could be freely distributed without licensing concerns.

“With QGIS there came the add-ins which made it powerful out of the box. You did not need any licenses. This is open source, it is easily distributable, everybody can download it and install it on their mobile device.”

When Patrice needed QFieldCloud access, he contacted OPENGIS.ch. The response was immediate and supportive — the only request was that the project link back to OPENGIS.ch on their webpage.

“I responded with creating a complete page about using this software. It’s the best thing I can do also for OPENGIS.ch in promoting their software and promoting this cloud service.”

More Than Just Mapping

For Patrice and the volunteers, the project serves purposes beyond safety and historical preservation. These tangible remnants of the Nazi era provide opportunities for communities to engage with history in meaningful ways.

“It is also a contribution to maintaining democracy. Here along the western border, everybody can get in touch with that history with concrete remnants around their own village.”

When history is local—visible in the woods near home rather than distant and abstract, it becomes personal. People become curious about their own community’s past.

“The people understand that it is a part of their identity and they are willing to view it also as a part of identity even if it has a dark history. It’s not my fault but it is my responsibility for maintaining it.”

Looking Ahead

What once required Patrice to manually process data from each person now scales effortlessly across dozens of contributors spread across hundreds of kilometers.

“I can in that way suit not 10 people, it can be 20, it can be 50, it can be 100. It doesn’t matter. That makes the process scalable.”

Technology hasn’t just made the work more efficient, it’s made an entire category of conservation and historical preservation possible that simply wouldn’t exist otherwise.

“Now I can just give someone a project of all objects in 20 kilometers around their home. We can collect data in a standardized way. You end up with a standardized database that you can start to query, that you can start to use for interesting analysis.”

About the Project:

The West Wall (Siegfried Line) fortification mapping project is coordinated by volunteer monument preservation groups working alongside German federal monument services, documenting and monitoring approximately 10,000 remaining WWII-era fortifications across a 600-kilometer zone along Germany’s western border.

Trusted Mobile GIS for Peacekeeping and Humanitarian Response

The United Nations Open GIS Initiative , a global partnership led by the UN Geospatial Operations team, has officially recognized QField as a recommended open-source solution for mobile geospatial data collection in UN field operations.

As part of its commitment to building a modern, scalable, and cost-effective open geospatial infrastructure, the Initiative evaluated several open-source tools and identified QField as a robust and flexible mobile platform well-suited for the realities of UN peacekeeping and humanitarian missions.

Why QField?

Field-tested by GIS teams in missions such as UNMISS (South Sudan) and MONUSCO (DR Congo) , QField proved highly effective for collecting, editing, and managing geospatial information in low-connectivity and challenging environments.

Its tight integration with QGIS, offline-first design, and customizable forms made QField a preferred solution in the Geo-Data Collection Working Group. In combination with PostgreSQL/PostGIS and GeoServer, QField helps field officers collect accurate, structured, and synchronized data across the UN’s global operations.

Key Benefits for UN Missions

• Reliable offline operation in low-bandwidth and remote settings
• Direct synchronization with centralized UN Spatial Data Infrastructure
• Support for custom forms, photos, UUIDs, and quality assurance
• Open licensing, enabling capacity-building and technology transfer

🛰️ UN Recognition for QField’s Strategic Role

“QField has been spotlighted by the UN Open GIS Initiative as a crucial part of their hybrid GIS system. This recognition underscores QField’s superior capabilities in supporting multiple United Nations Sustainable Development Goals (SDGs) and revolutionizing geospatial data collection.”

This endorsement reflects the UN’s confidence in QField as a scalable, flexible, and mission-critical mobile application capable of serving a wide range of geospatial needs — from emergency operations and environmental monitoring to infrastructure mapping and humanitarian support.

Beyond Software: Capacity Building and Openness

In alignment with the UN’s open-source strategy, QField has been integrated into training curricula for GIS officers across multiple UN agencies. The Initiative emphasized not only deploying tools, but also building local capacity and skills — with over 120 UN staff trained in QGIS and QField through collaborations with Politecnico di Milano and OSGeo’s GeoForAll network.

A Global Signal for Open Source

By choosing QField and other FOSS4G tools as part of its official technology stack, the UN Open GIS Initiative sends a strong message to governments and humanitarian actors worldwide: open, mobile-first geospatial tools are ready for mission-critical deployments.

Learn More

• UN Open GIS Official Website
• ISPRS 2021: UN Open GIS Paper
• QField Project Website

The main objective was to allow operators to access in the field the graphic and alphanumeric data on trees, shrubs, hedges, turf and street furniture elements in offline mode both in reading and editing mode with the return of these data in GINVE.CLOUD via a synchronisation procedure.

For this purpose, it was decided to exploit the potential offered by QField and the GeoPackage database.

Preliminary project activity

Initially, a procedure was set up in GINVE.CLOUD to generate a Geopackage and the corresponding QField project file from the GINVE.CLOUD platform.

The Geopackage produced was structured to allow the management and mapping of data, supporting the insertion of point elements, lines, polygons and photos. In addition, form fields with customised attributes and value maps, value relations and check boxes were prepared in order to simplify data input by users.

In particular, the trees layer has been prepared to allow the management of forms for the collection of the following data: - Identification data - Dimensional and Qualitative - Notes-Other data - Damage - Interventions - Interference

Themes and labels were customised for each layer to make them similar to those in GINVE.CLOUD.

In addition to customising the data fields, special procedures for displaying the base map were created. Google Maps and OpenStreetMap were used as the base map, but the structure was prepared to allow the use of other raster maps so that they can be displayed and managed in QField.

Data Entry

The data entry activity refers to the possibility of entering data relating to the position of the element on the map, the compilation of the element’s master data sheet, with photography and planning of interventions.

The graphic data were entered using both automatic positioning via GPS and manual positioning using the positioning functions offered by QField. The alphanumeric data were entered by filling in specific survey sheets with differentiated data according to the element selected.

The collected data was then imported and synchronised with GINVE.CLOUD through a specific procedure that allows the verification of data (even from multiple operators) and alerts the user of any problems encountered, providing details of the error in order to facilitate its correction by the operator. This procedure also allows the import of photos that have been taken byQField and their automatic storage in the Cloud.

Results

Graphic and alphanumeric data were exported directly from GINVE.CLOUD into QField for immediate mobile use and management by operators. QField’s feature of being completely offline usable coupled with the possibility of predefined filling in of certain fields allowed data entry activities to be speeded up, reducing the possibility of human errors occurring and allowing users to be more efficient during census activities. Any conflicts with data in GINVE.CLOUD were handled during synchronisation by allowing the operator to choose and validate which data should be stored. Data imported from QFields are immediately available to all GINVE.CLOUD operators.

Thanks to QField, it has been possible to achieve new goals, enabling users of GINVE.CLOUD to use a high-performance and intuitive solution that provides continuity to the activities carried out in the field while guaranteeing maximum operational efficiency.

In addition, the integration enabled us to achieve the following objectives: - Use of maps and offline data on Smartphones and Tablets - Increased speed in data entry activities- Full compatibility with GINVE.CLOUD - Direct import of Geopackage from GINVE.CLOUD- Portability of data in QGIS - Data usable on other GIS platformsThe integration with QField represents an important step in the growth of GINVE.CLOUD and demonstrates its high readiness for interfacing with modern open source applications that make use of innovative, state-of-the-art technologies.

To conduct data collection of all rural water supply network in Rwanda, and keep updating the data continuously in order to improve operation & maintenance of waterworks.

Project preparation

Before starting our data collection, we conducted the following things: – Develop our own PostGIS database – Develop QGIS project template with Geopackage. The Geopackage table design is equal to PostGIS to be able to copy and paste to PostGIS.

Apart from preparing Android devices, we purchased GPS devices for higher positioning accuracy. In WASAC, we bought Garmin GPSMAP 64S. Sometimes, GPS of smartphone and tablet is not very accurate, so we normally capture the same location by using Garmin GPS, then correct the location of QField’s data after data collection work.

Data collection

Once we prepared Geopackage and QGIS project template, we conducted training of QGIS/QField in July 2018 and launched our data collection work in 27 districts in the whole country of Rwanda. 27 engineers sent their Geopackage to the central office in Kigali. the MIS (Management Information System) specialist validated and entered their data from Geopackage to PostGIS database. We completed our initial data collection works until April 2019.

Data collection procedure

Data distribution and updating

The most significant thing after data collection is updating. We have seen many organization in Africa, which failed to keep data up to date. Several years later, their data will normally become too old, and most of them need to put efforts on data collection again. WASAC decided to continuously update all of the data and keeps doing this until now. QField has proven to be very well suited for this purpose. In order to distribute and updating the data, we developed a python script postgis2qfield. This postgis2qfield tool can extract the data from PostGIS and create Geopackages for each district in Rwanda. We upload these 27 geopackage together with QGIS project template to Google Drive storage. After that, those engineers in districts download their geopakage to Android device to continue adding and updating the data. Once they completed updating, they sent the geopackage to central office again, MIS specialist update PostGIS database and regenerate geopackages for QField.

Data distribution and updating procedure

Data sharing via vectoriles

First of all, you can see our collected data from here .

Since July 2020, we started to distribute our water supply system’s data via vectortiles as open data. Although Rwanda's internet situation is being improved, some rural area still have problems of internet. In such as poor internet situation, WMS or WFS data distribution will not work well. Vectortiles can provide light and fast distribution of map data. We will not talk about our vectortiles here. If you are fascinated by how to share the result of data collection, please also see this instruction .

Acknowledgement

We thank all of district water and sanitation support engineers to conduct their data collection work. Additionally, we want to thank the developers of QField and QGIS for offering fantastic open source software. It is great that, due to free software, such projects can be implemented by an organization of water sector in developing countries.

About WASAC

WASAC has 2 main departments for urban water(UWSS) and rural water(RWSS). We are using QField in RWSS. The role of RWSS department is to support local government to operate and maintain their owned water supply systems in rural area. Nowadays, these data collected and maintained by RWSS department are being used by more than 30 private operators in 27 districts. Total number of water supply systems in the database is 1,000+.

Organogram of WASAC

Also, one of our colleagues presented WASAC's activity in FOSS4G 2019 Bucharest. Although some of system were little bit changed now, you can also see this video if you are interested.

Disaster response teams tasked with saving cultural heritage face the enormous challenge of documenting damage, tracking rescue actions, and ensuring efficient data handling—often under harsh conditions. The KulturGutRetter project, led by DAI in partnership with THW and LEIZA, set out to build an open-source workflow for real-time data acquisition and management during emergency missions

QField in Action

At the heart of the system is QField , integrated with QGIS and tailored form-based templates that capture a wide variety of data types—photographs, audio, video, geolocation, and unique UUID/QR-coded IDs. This ensures every asset—whether building or artifact—can be traced from salvage through stabilization

Field-Tested & Ready for Deployment

QField-powered field kits equipped with tablets connect via a local server and router—even offline. Synchronization to a central PostgreSQL database allows live tracking and coordination of data across devices. This mobile GIS system proved its resilience during a full-scale disaster drill at Demerthin Castle (September 2024), fulfilling ISO-standard data capture and secure workflow needs

Key Features

• Form-based input with geolocation, photos, audio/video, UUID/QR linking
• Offline-capable local network sync to a central mini‑server
• Modular workflows for both movable and immovable heritage elements
• Automatic image syncing via local web service, and full data export in FAIR-compliant formats
• Adaptable system—online, offline, paper backup modes to suit any emergency scenario

Impact & Takeaways

This integrated QField‑based system allowed the KulturGutRetter team to:

• Quickly record structured, high-quality data during heritage rescue operations,
• Coordinate seamlessly in offline environments,
• Maintain data integrity from field entry through centralized storage,
• Prepare exportable packages—including 3D scans and field metadata—for future conservation and analysis

Built on Open Source, Ready for the Future

By combining QField, QGIS, PostgreSQL, and open‑source tooling under the KulturGutRetter project—supported by the German Bundestag and Foreign Office—this case demonstrates how agile, mobile-first open-source GIS can revolutionize emergency documentation for cultural heritage

Learn More

• KulturGutRetter: Data Acquisition & Management
• Full-scale field exercise (Sept 2024)
• Interview with DAI on QField deployment

Developed over several years and officially rolled out in spring 2025, MTTJ gradually replaces legacy proprietary systems with a modern, efficient, and extensible solution tailored to NLS’s evolving needs — all while promoting transparency, interoperability, and long-term sustainability. As part of this open architecture, QField will play a key role , empowering field teams to collect and validate data efficiently, even in remote locations. Use of QField is being piloted over summer 2025 and will be gradually taken up for full production use towards the autumn.

“QField lets our field staff work smarter and faster, even in the most remote corners of Finland. It’s a vital part of our open-source geospatial infrastructure.” — Jani Kylmäaho, Director of Development and Digitalization, National Land Survey of Finland

Throughout the development of MTTJ, the National Land Survey of Finland actively engaged with the QField project, contributing not only feedback and use cases, but also procuring for the development of new functionalities key to NLS use cases to QField core. This close collaboration ensured that QField evolved to meet the demanding requirements of national-scale field data workflows and continues to benefit the wider open-source geospatial community.

Built for Performance, Collaboration, and Openness

The new system supports 100–150 concurrent operators, integrates photogrammetry tools, and offers robust real-time quality assurance and job management features. The architecture is centered on QGIS, PostgreSQL/PostGIS , and a custom set of plugins and APIs designed to streamline workflows from aerial imagery to finished topographic data

Among its standout features:

• Task and conflict management tools directly within QGIS
• Workspace-based editing to prevent data collisions
• Tight integration with QField for field data collection

All core components were built with open-source principles in mind — and many will be shared with the global QGIS and OSGeo community.

Seamless Field-to-Database Workflow

Finland’s mapping authority integrated QField and QFieldCloud to allow field staff and aerial image interpreters to:

• Collect and verify topographic features onsite,
• Access up-to-date maps and imagery offline,
• Digitize observations with domain-specific presets,
• Sync edits back to central databases using open standards.

This QField-enhanced workflow helps ensure high positional accuracy, real-time feedback, and consistent data quality , even when operators are far from headquarters.

Designed for Professionals, Chosen by a Nation

The NLS chose QField not only for its powerful offline capabilities and QGIS compatibility, but also because:

• It offers an intuitive user interface,
• It offers powerful functionalities,
• It enables field validation workflows,
• It’s adaptable, multilingual, and field-tested.

By integrating QField into a national strategy, Finland has showcased how modern, mobile-first open-source tools can outperform legacy systems — all while reducing costs and increasing flexibility.

Learn More

• Official NLS announcement
• Article in Positio Magazine
• FOSS4G Europe 2024 talk by NLS
• FOSS4G Europe 202 talk by NLS

Communities in Fiji rely on landscape resources for agricultural and forestry-related activities. Accurate mapping and monitoring patterns of land use and land cover (LULC) over time at an appropriate scale is important for informing landscape management, policies, and climate-smart sustainable development. Fiji’s Ministry of Forestry is collaboratively developing an approach with the Universities of Sydney (USYD), Western Australia (UWA) and the South Pacific (USP) to produce an inter-annual LULC map using Sentinel-2 satellite data, and freely available geospatial tools. QFIeld is being used for collecting ground truth data in the landscape for training and validation of the LULC map.

Workflow

– The LULC ground truth collection form was designed in QGIS. – A set of predefined ground truth plot locations were generated based on a stratification of satellite data within the study area. – The form, predefined plots, and appropriate offline background layers were packaged in QGIS and then loaded onto each of the tablets used by the field team (Figure 1). – A team member navigated to a predefined plot in QField and created a ground truth point at the location and labelled the point with the most appropriate pre-defined LULC class (Figure 2). – Data collected from all tablets was combined into one ground truth data collection in QGIS. – Image interpretation using the OpenForis platform will be used to increase the number of ground truth plots. – The final ground truth collection will be imported into Google Earth Engine to produce the LULC map and calculate the map accuracy.

Figure 1: Predefined Ground Truth Plots

Figure 2: Capturing land cover class

Preliminary Results and Future Work

An example of a preliminary land cover map is shown in Figure 3. An important objective from our work is to transfer skills and build capacity with local stakeholders to continue to update the LULC map on an annual basis as well as to expand the map to include other communities, catchments and forestry areas across Fiji. This capacity building will include iterative stakeholder consultation, online training materials, field and classroom training workshops, and collaborative fieldwork.

Figure 3: Preliminary land cover map for the Ba region, Viti Levu, Fiji

Acknowledgment

We would like to thank the field team from the Fiji Ministry of Forestry especially Viliame Tupua and Renata Varea (USP). The project was funded by the Australian Centre for International Agricultural Research (ACIAR; ASEM/2016/101).

The Fiji Forestry/USP field team is about to collect ground truth data with QField.

The Mission: FUSRAP Site Remediation

Working under the Formerly Utilized Sites Remedial Action Program (FUSRAP), geological specialists lead cleanup efforts for low-level radioactive waste sites across the country.

“We’re cleaning up radiation, in simple terms,” explains the project geologist. “Back in the 60s and early 40s, we didn’t really know what this material could do. We’re dealing with radium, uranium, thorium—materials that were processed for the atomic bomb program.”

The cleanup process requires precise detection and mapping of contaminated areas, followed by careful excavation and disposal.

The Challenge: Real-Time Data Collection in Hazardous Environments

The team works in challenging conditions, dressed in full personal protective equipment including respirators while operating sensitive radiation detection equipment. Before implementing QField, their workflow created significant delays that hampered cleanup operations.

“We used to have a three day processing time where we would have breadcrumb trails of data, but we wouldn’t be able to actually see what those breadcrumbs represented in terms of gamma data,” recalls the specialist. “We’d have to bring our data back, scan, come out of the zone, bring it back to my desk, and go through this long processing system through three different software packages.”

This delay meant field crews would scan an area, then wait days for analysis before knowing where to dig or whether an area was clean. The old system created bottlenecks that frustrated operations teams eager to begin excavation and backfilling.

QField Integration: Connecting Sensors to Real-Time Mapping

The breakthrough came through integrating radiation detection instruments directly with QField. Working with the QField development team, the specialist created a system that captures gamma radiation readings every second while simultaneously recording precise GPS coordinates.

This sensor integration capability was initially developed specifically for QField to meet the growing demand for real-time sensor data collection in field applications. However, following open-source best practices, OPENGIS.ch contributed this functionality upstream to the broader QGIS project, making sensor integration available to the entire QGIS ecosystem.

“We basically just measure the offset from our detector bottom to the antenna to get our elevations,” explains the specialist. “The detector spits out data every second, and QField captures both the radiation reading and the location simultaneously.”

The team uses multiple radiation detection methods: - Portable detectors connected via Bluetooth or USB to field tablets - Advanced spectrometers ($90,000 units) that can identify specific radioactive isotopes - Mobile scanning systems mounted on small carts for efficient area coverage

Using QField’s sensor integration capabilities, radiation data flows directly into the mapping interface with custom delimiters to parse the incoming data stream properly.

Transforming Field Operations

The impact on field operations has been dramatic. What previously took three days of processing time now happens in real-time, allowing crews to make immediate decisions about sampling and excavation.

“This process used to take, I would say on average, three days. Working four days a week left us maybe able to do one backfill per week. Now we can do two backfills a week. We can do a scan and collect samples within an hour—we never thought we could ever do that.”

Precision and Coverage Verification

QField’s mapping capabilities also solve critical quality control issues. The team can now see their coverage in real-time, identifying gaps that might have been missed during scanning.

“You can see your sensor being established in real-time. We used to always lose connection to our sensor and then I’d have to go figure out which COM port it’s on. Now I can see our sensor readings coming in, and I can also spot if I miss an area and fill it in before we finish.”

With horizontal accuracy running at four millimeters using base station corrections, the team meets the stringent precision requirements for radioactive waste cleanup.

Data Integration and Workflow

Field teams use ruggedized tablets running QField to collect data throughout their scanning operations. The system integrates multiple data types: - Gamma scan data: Real-time radiation readings with GPS coordinates - Sample locations: Precise positioning for soil samples sent to on-site laboratories - Excavation boundaries: Mapping areas cleared for backfilling - Infrastructure: Locations of monitoring equipment, access routes, and safety hazards

Results are processed through the on-site laboratory within 24 hours, allowing rapid decisions about whether areas can be backfilled with clean soil or require additional excavation.

Looking Forward

The success of QField integration has transformed how the team approaches radioactive waste cleanup. They can now respond immediately to detection data, coordinate multiple field crews efficiently, and provide real-time updates to regulatory agencies overseeing the cleanup efforts.

“It’s really been upping our game here,” concludes the specialist. “The ability to visualize this data in real-time, combined with the precision GPS and immediate feedback, has completely changed how we operate in the field.”

In January 2022 the Hunga Tonga-Hunga Ha’apai submarine volcano erupted. Ash clouds from the eruption and the subsequent tsunami damaged croplands on the Tongatapu and Ha’apai island groups.

Croplands damaged after Hunga Tonga-Hunga Ha’apai eruption.

Activities

Analysis of Tonga Crop Survey Data

Ministry of Agriculture, Food, and Forests staff used the Tonga Crop Survey data, which was collected using the maplandscape workflow in the months before the eruption, to perform an initial estimate of damage to the food supply.

Monitoring of the agricultural recovery

The maplandscape workflow uses QField for mobile data collection. QField has full online and offline modes; this enabled Ministry staff to conduct damage mapping activities and conduct surveys to monitor how government resources for recovering croplands were being used while the countries internet was cut off. The internet cable to Tonga was severed during the eruption. In particular, QField was used to generate maps of where fields had been cleared and re-ploughed using government support. The eruption severed the undersea cable which connected Tonga to the internet.

Damage assessment with community leaders

In the months following the eruption, Ministry staff used maplandscape to interview community leaders in areas affected by the eruption to ascertain loss and damage to croplands, harvested crops, and agricultural infrastructure. This information was used to compute amounts for relief payments.

Trees damaged after Hunga Tonga-Hunga Ha’apai eruption.

Outputs

• An early estimate of croplands and agricultural produce affected by the eruption.
• Monitoring of the use of government resources and support for recovery of croplands.
• Dataset of loss and damages to the agricultural sector in affected communities.

The Challenge: Managing Conservation at Scale

ZIP’s mission is breathtaking in scope: to completely remove invasive predators from New Zealand as part of the nationwide Predator Free 2050 initiative. These predators devastate native ecosystems, killing an estimated 25 million native birds annually.

In South Westland alone, their project area spans 114,000 hectares of challenging terrain. This extraordinary achievement represents a vast predator-free area, protected on all sides by mountains, rivers, the ocean, and a network of remote reporting traps and detection devices.

As a frame of reference - “The scale of the project area is extraordinary: 10 times the size of New Zealand’s largest predator-free island, and over 30 times the size of the largest fenced sanctuary.”

Waitangitahuna River on the left and Whataroa from Mt Price

Tracking 20,000 Devices in the Field

The scale of data management required for this operation is immense. ZIP manages approximately 20,000 field devices through QField in their South Westland project alone—including trail cameras, AI cameras, bait stations, and cage traps. Each device requires ongoing servicing, maintenance, and data collection.

For each device location, the team needs to record three types of information: Actions: Deployment of equipment and setup details Events: Servicing information and maintenance records Properties: Operational data such as bait take, lure status, and battery levels

Before QField, this information was captured using Garmin GPS devices and paper notebooks—a cumbersome process that created significant delays in data processing and limited the effectiveness of rapid response operations.

The Evolution to Digital Data Collection

ZIP’s journey to QField began with a creative but limited solution—using Garmin GPS units to manually record data in JSON format within text fields. While innovative, this system showed its limitations as operations expanded from a 400-hectare pilot site to 12,000 hectares of challenging terrain.

“The GPX capture was starting to show its limitations, and using notepads to record data was pretty much showing its limitations,”

After researching digital field data collection options, ZIP chose QField for its seamless integration with their existing open-source GIS stack (QGIS, PostgreSQL, and GeoServer), its robust offline capabilities, and the rich functionality it offered.

“The ability to control symbology and just the amount of data that could go out in a packaged map on the phone was fantastic,”. “It worked so much better than Garmins and paper notes.”

QFieldCloud: Transforming Response Times

The introduction of QFieldCloud revolutionized ZIP’s field operations. Before QFieldCloud, data synchronization involved physically collecting phones from field staff every two weeks, manually connecting each device to a computer, and processing the accumulated data—causing significant delays in data availability.

“The data turnaround was around about two weeks. You record on your phone for two weeks and then sync it,”. “There was a bunch of things that happened in the back end, like SQL scripts to refresh symbologies.”

Now, with QFieldCloud, field staff can sync their data daily, providing near real-time information flow critical for responding to predator detections.

“If we’ve cleared an area of predators and then we get a predator appearing inside our block, we have to be able to react to that very quickly. If it’s a possum, if you’re not onto that possum within a couple of weeks, it’s likely moved 20 or 30 kilometers from where you found it. Being able to respond as quickly as possible is sort of the key to success for us.”

This rapid data flow has transformed their ability to coordinate teams and respond to incursions efficiently.

Field Team Coordination

ZIP has approximately 80 staff members across the organization, with about two-thirds working in field-based roles. At any given time, around 35 active QField users are collecting and accessing data across multiple project sites.

When responding to a predator detection, teams work intensively in a targeted area. QFieldCloud enables staff to see exactly what has been done and what remains to be done, eliminating duplication of effort and improving coordination.

“The ability to go out, sync their data, and then go out the next day and go, ‘okay, this is the section that’s left to do’—that’s been game-changing,”.

Field staff also use QField to access critical information in the field, including: - Trap and bait station locations - Device deployment details - Health and safety information (tracks, wasp nests, mine shafts) - Predator sighting locations

Looking to the Future

As ZIP continues to expand their operations—including beginning work on Stewart Island (Rakiura), a 170,000-hectare project—the scalability of QField becomes increasingly valuable.

The organization remains committed to the open-source technology stack that supports their conservation work. “We’re very appreciative of the open-source stack that supports all of our work,”. “The amount of data that we’re housing—our detection records are in the tens of millions—and that’s all on PostgreSQL and GeoServer.”

📷 Photos taken by Chad Cottle