Mobile Laser Scanning for Digital Cemetery Mapping

Transforming complex cemetery environments into georeferenced, platform-ready point clouds and intuitive 3D spatial representations.

Jul 27, 2026

CASE STUDY · REALITY CAPTURE

Integrated colourised point cloud created from multiple mobile scanning trajectories and prepared for downstream cemetery management workflows.

PROJECT OVERVIEW

Three municipal cemetery sites across two rural municipalities in Hungary were digitally documented to support cemetery mapping and the online registration of burial plots.

I worked as part of a two-person project team in collaboration with RoaData Kft., contributing to both field acquisition and the downstream point cloud workflow.

Multiple mobile scanning trajectories had to be processed, registered, georeferenced, cleaned and optimised into unified datasets suitable for the target cemetery management environment.

PROJECT TYPE: Municipal Spatial Documentation
MY ROLE: Capture · Processing · Registration · Georeferencing · QA/QC
OUTPUT: Platform-Optimised Point Clouds · 3DGS
COLLABORATION: RoaData Kft.

THE CHALLENGE

Cemetery environments contain dense and irregular geometry. Narrow pathways, mature vegetation, monuments and closely positioned graves create significant occlusion during mobile data capture.

The project also introduced processing challenges. Independently captured parcels required sufficient overlap for reliable registration, while the final point clouds had to maintain geometric detail and positional accuracy within the technical limitations of the target platform.

The challenge was therefore not simply to capture as much data as possible, but to create spatial datasets that were accurate, coherent and usable for their intended application.

Dense vegetation, irregular burial plot layouts and restricted visibility created challenging conditions for mobile data capture and trajectory planning.

MY ROLE

I worked as part of a five-person project team in collaboration with RoaData Kft., covering both assigned field acquisition and downstream point cloud processing.

My responsibilities included mobile scanning, trajectory overlap planning, point cloud cleaning, parcel integration, registration in RiSCAN PRO, GNSS-supported georeferencing refinement, colourisation, point density optimisation and final QA/QC.

My involvement therefore extended from field capture through to the preparation of project-ready spatial datasets.

THE TECHNICAL APPROACH

Each cemetery was divided into smaller survey parcels and captured using handheld mobile laser scanning.

Because the workflow relied on continuous trajectories rather than fixed scan positions, sufficient overlap between neighbouring survey areas was essential for reliable downstream registration.

GNSS-measured control points provided an independent spatial reference for refining scanner-derived EOV coordinates.

The capture strategy was therefore designed around the requirements of the processing workflow:

Capture → Maintain Overlap → Establish Control → Register → Georeference

Dense vegetation and narrow spaces required slower, more deliberate scanning trajectories. The same conditions increased downstream cleaning effort by generating significant unwanted point cloud data around burial plots.

Mobile field acquisition across complex cemetery environments required adaptable survey trajectories and careful spatial coverage.

POINT CLOUD PROCESSING

Individual trajectories were processed in Lixel Studio and transferred to RiSCAN PRO for registration and integration. Overlapping geometry between neighbouring survey areas supported alignment, while GNSS control points were used to refine the scanner-derived EOV coordinates.

Processing → Cleaning → Alignment → Registration → Georeferencing → Colourisation → Optimisation → QA/QC

The objective was to transform independent scan datasets into coherent, project-ready spatial data.

Multiple independently captured trajectories were registered into a unified spatial dataset.

Colour-coded trajectory datasets supported registration, integration and quality control.

DATA OPTIMISATION FOR THE TARGET PLATFORM

The completed point clouds contained more data than the target platform could efficiently process. The datasets were therefore filtered and downsampled while preserving the geometry required for practical documentation.

Approximately 1 TB of processed point cloud data was handled across the project.

The highest possible point density is not always the most useful deliverable.

Point density was reduced to meet platform limitations while preserving relevant cemetery geometry.

ADDITIONAL 3D VISUALISATION

Selected environments were also represented using 3D Gaussian Splatting (3DGS).

While point clouds remained the technical reference dataset, 3DGS provided a more intuitive visual layer for non-technical stakeholders.

3DGS provided an intuitive visual layer alongside the engineering point cloud dataset.

THE OUTCOME

The workflow transformed multiple independent mobile scanning trajectories into unified, georeferenced and platform-optimised spatial datasets.

Registered and integrated point clouds
Cleaned and colourised datasets
EOV georeferenced data
Platform-optimised point clouds
3DGS visualisations

*Final integrated spatial dataset prepared for downstream municipal documentation.*

WHAT THIS PROJECT DEMONSTRATES

This project demonstrates that reality capture value is created not only during field acquisition, but during the processing stages that follow.

Registration, cleaning, georeferencing, QA/QC and optimisation were essential to turn fragmented scan data into reliable, project-ready datasets. This is directly relevant to teams that already have captured data but need specialist processing capacity.