Practical point-cloud processing training for drone teams, survey groups, engineering organizations and technical professionals. Build a repeatable workflow from data intake and coordinate-system setup through classification, quality control and export.
LiDAR acquisition is only the beginning. A defensible result depends on correct project setup, coordinate reference, trajectory and calibration review, point-cloud generation, classification, quality control and an export structure suited to the intended analysis.
Recognize the role of raw observations, trajectory information, base or correction data, control, imagery and project metadata.
Configure the supported processing workflow, review calibration context and generate a working dataset without losing traceability.
Separate ground and non-ground features, remove noise, identify anomalies and refine the dataset for the required output.
Apply documented QA/QC checks, review limitations and export files that downstream users can understand and reproduce.
Each step is connected. An incorrect coordinate system, missing trajectory file or undocumented filter can affect everything that follows.
Confirm file completeness, acquisition notes, sensor configuration, coordinate information, corrections and expected outputs.
Establish units, coordinate reference, geoid or vertical context, project organization and naming conventions.
Understand supported GNSS/IMU or PPK inputs, correction data, time synchronization and available trajectory diagnostics.
Apply supported processing settings, colourization or camera inputs, calibration context and initial noise filtering.
Review strips and surfaces, classify ground and features, remove noise, compare control and investigate anomalies.
Prepare LAS/LAZ, terrain or surface products, reports, metadata and project files appropriate to the agreed workflow.
Programs can be delivered individually or combined into a progressive learning path. Final modules depend on the software, sensor, dataset and required deliverables.
For participants who need a clear understanding of LiDAR data, files and processing concepts before working through a full project.
For teams that need a repeatable workflow from raw sensor data through a reviewed, classified and exported point cloud.
For users who already generate point clouds and need stronger classification, surface creation, cleanup and deliverable preparation.
For organizations that need consistent settings, review gates, file structures and deliverables across multiple processors or projects.
Select the closest challenge to see a recommended starting point.
The goal is not memorizing a sequence of clicks. Participants should understand what each input, setting and quality check changes in the final dataset.
Horizontal and vertical reference concepts, units, transformations, geoid context and common setup errors.
Supported GNSS/IMU inputs, base or correction data, time alignment and trajectory-quality review.
Calibration context, boresight concepts, strip review, overlap differences and identifying systematic artifacts.
Ground and non-ground workflows, automated results, manual refinement, class codes and project-specific criteria.
Control or checkpoints where applicable, surface review, density, gaps, noise, residuals and documented limitations.
LAS/LAZ, terrain and surface products, contours or profiles where supported, metadata and clear handoff documentation.
Software scope, supported data formats and licensing must be confirmed before the session. Training can use a representative sample dataset or an approved client project.
Supported DJI LiDAR project setup, point-cloud processing, reconstruction options and export workflow.
Supported drone LiDAR processing, point-cloud management, classification and quality-control workflow.
Supported production modules, classification, surface and feature workflows for applicable environments.
Point-cloud inspection, comparison, measurements, segmentation and complementary open-workflow techniques.
Define which application owns each processing, classification, QA and export stage.
Apply the approved workflow to a representative dataset while documenting decisions and limitations.
The instructor needs enough project context to determine whether a dataset is suitable for the requested exercises and whether additional inputs are required.
LiDAR processing varies by sensor, acquisition method, software, coordinate reference and the quality standard required.
Yes. The fundamentals program is designed for new users. Participants seeking raw-data processing or advanced classification may need prior point-cloud, GIS, surveying or software experience.
The scope can be considered for supported workflows involving DJI Terra, LP360, Terrasolid, CloudCompare or a defined multi-application process. Exact software, version, licence and module availability must be confirmed.
Yes, after a suitability and confidentiality review. A representative subset may be recommended so the training remains focused and can be completed within the scheduled time.
This page focuses on processing. Acquisition planning or aircraft training can be added through a broader enterprise training scope when required.
No. Accuracy depends on acquisition, sensor calibration, positioning, control, coordinate reference, processing and validation. Professional surveying requirements and responsibility must be determined for the project and jurisdiction.
Yes, where supported by the selected software and dataset. The scope can include automated classification, manual refinement, surface review and applicable terrain or surface outputs.
Remote instructor-led delivery may be suitable when participants have the required software, licences, data, workstation capability and screen-sharing access. In-person and blended delivery can also be considered.
Requirements depend on the software, dataset size and processing functions. Provide workstation details during intake so storage, memory, graphics and licensing limitations can be reviewed before scheduling.
Share the sensor, software, participant experience and desired deliverables. Unmanned Canada can recommend a suitable processing-training scope.
A reliable deliverable starts with the right acquisition system and continues through a documented processing and QA workflow.
