LiDAR Drone Mapping Training Course

Participants can select and describe LiDAR system components against project requirements.

• LiDAR sensor specifications: pulse rate, scan angle, range and returns
• GNSS receivers, inertial measurement units and timing synchronisation principles
• Beam divergence effects on footprint size and small-feature detection
• Scanning mechanisms: rotating mirror, oscillating mirror and solid-state systems
• Payload mass, endurance and aircraft stability constraints for mission design
• Reading manufacturer specifications without confusing precision, accuracy and resolution

Participants can establish coordinate and vertical reference requirements for LiDAR surveys.

• Horizontal datums, projected coordinate systems and local grid transformations
• Vertical datums, geoid models and ellipsoidal height conversion methods
• Control-point design for LiDAR calibration and independent accuracy testing
• RTK and PPK positioning compared for airborne LiDAR survey control
• Base-station observation intervals, logging formats and antenna-height records
• Checkpoint independence maintained from calibration and processing control points

Participants can convert client requirements into a measurable LiDAR survey specification.

• Defining area boundaries, feature classes, densities and required accuracies
• Client questions that expose terrain, canopy, access and timing constraints
• LAS classification schema aligned with intended mapping products
• Deliverable schedules for point clouds, rasters, contours, reports and metadata
• Acceptance criteria based on completeness, classification quality and positional accuracy
• Risk register covering weather, airspace, control access and data loss

Participants can prepare flight plans that meet density, overlap and safety requirements.

• Calculating point density from altitude, speed, pulse rate and scan angle
• Flight-line spacing determined by swath width and planned sidelap
• Terrain-following plans for relief, obstructions and variable ground elevation
• Cross-lines designed for strip alignment and calibration verification
• Selecting altitude and speed for vegetation penetration and feature detection
• Airspace, weather, battery and emergency constraints documented before deployment

Participants can manage field acquisition records and identify conditions affecting data quality.

• Pre-flight sensor warm-up, timing checks and storage capacity verification
• GNSS satellite geometry assessed using PDOP, visibility and outage forecasts
• Field logs capturing control observations, flight identifiers and weather conditions
• Boresight calibration flights planned with opposing headings and varied roll
• Monitoring aircraft speed, scan angle and sensor status during collection
• Post-flight data backup using verified copies and structured folder conventions

Participants can interpret trajectory processing outputs and diagnose positioning weaknesses.

• Raw GNSS and IMU observations combined through Kalman-filter trajectory processing
• PPK baseline solutions checked for fixed ambiguities and observation gaps
• Trajectory plots reviewed for position uncertainty, velocity spikes and dropouts
• Time synchronisation errors identified through systematic point-cloud displacement patterns
• Lever-arm offsets between GNSS antenna, IMU and LiDAR sensor applied
• Exported trajectory files documented with datum, epoch and processing settings

Participants can generate georeferenced point clouds and evaluate boresight calibration results.

• Range measurements transformed using trajectory, attitude and sensor mounting parameters
• Boresight angles estimated for roll, pitch and heading misalignment
• Strip alignment assessed through overlapping surfaces and cross-line comparisons
• Planar roof surfaces used to detect systematic elevation and tilt errors
• Point-cloud density visualised by grid statistics and flight-line identifiers
• LAS and LAZ formats managed with coordinate, scale and offset settings

Participants can organise, inspect and document raw and processed LiDAR datasets.

• Tile naming conventions based on grid references, dates and processing versions
• Point-cloud attributes inspected: intensity, return number, classification and GPS time
• CloudCompare tools used for visual inspection, profiles and distance measurements
• Outliers identified through elevation histograms, density maps and profile views
• Metadata records capture sensor settings, coordinate systems and processing lineage
• Storage estimates calculated for raw files, intermediates and final deliverables

Participants can select and assess ground-classification methods for varied terrain conditions.

• Progressive TIN densification separates candidate ground points from above-ground returns
• Cloth Simulation Filter parameters adjusted for terrain and object scale
• Slope-based filtering applied to embankments, cliffs and urban surfaces
• Classification errors recognised beneath dense canopy, bridges and low vegetation
• Manual reclassification targeted through profiles, hillshades and suspect polygons
• Ground-class quality checked against surveyed checkpoints and reference surfaces

Participants can classify non-ground features and derive object-specific LiDAR information.

• ASPRS LAS classes assigned for vegetation, buildings, water and noise
• Height-above-ground normalisation supports vegetation and asset feature extraction
• Building points separated using planarity, height and connected-component rules
• Powerline detection considers point spacing, scan geometry and conductor sag
• Water surfaces identified through low return density and elevation consistency
• Classification confidence documented where automated rules require manual review

Participants can create terrain and surface models suited to specified mapping uses.

• Digital terrain models interpolated from validated ground-class point returns
• Digital surface models generated from highest-return or maximum-elevation cell values
• Grid resolution selected from point density, terrain complexity and intended use
• Breaklines incorporated where kerbs, channels and ridges require preservation
• Hillshade, slope and aspect rasters produced for terrain interpretation
• Contour intervals chosen from vertical accuracy and map-scale requirements

Participants can derive vegetation structure metrics from classified and normalised point clouds.

• Canopy height model calculated from surface and terrain elevation differences
• Height percentiles summarise stand structure within inventory plot boundaries
• Canopy cover estimated from return distributions and ground-return proportions
• Individual tree detection assessed against crown overlap and point density
• Understory and canopy layers separated using height-band classification rules
• Forestry outputs qualified by leaf condition, species structure and terrain

Participants can configure LiDAR outputs for linear infrastructure, earthworks and hydrological analysis.

• Corridor profiles extracted along centre lines with offset and elevation attributes
• Clearance analysis compares conductor, vegetation and structure elevation surfaces
• Stockpile volumes calculated using reference surfaces, boundaries and cut-fill grids
• Mine bench geometry inspected through slope, crest and toe measurements
• Hydrological conditioning removes spurious sinks from terrain model surfaces
• Floodplain cross-sections derived for channel, bank and overbank assessment

Participants can test LiDAR deliverables against survey tolerances and document findings.

• Independent checkpoints compared against ground-class elevations using residual calculations
• RMSEz, bias and percentile errors reported for vertical accuracy assessment
• ASPRS Positional Accuracy Standards applied to accuracy-reporting terminology
• Completeness checks identify gaps, low-density zones and unclassified areas
• Strip-to-strip differences measured across stable, planar overlapping surfaces
• Quality-control findings recorded with severity, evidence, correction and closure

Participants can design repeatable processing workflows using appropriate GIS and point-cloud tools.

• PDAL pipelines organise reading, filtering, reprojection, classification and writing stages
• QGIS integrates LiDAR-derived rasters, vectors, labels and map layouts
• Batch processing parameters controlled through versioned workflow documentation
• Tile-edge artefacts reduced through buffered processing and edge reconciliation
• Processing logs retain source files, settings, software versions and outputs
• Workflow decisions linked to survey specification and acceptance requirements

Participants can prepare procurement and governance controls for internally delivered or contracted LiDAR work.

• Statement-of-work clauses define coverage, density, accuracy, classes and formats
• Supplier proposals evaluated against sensor capability, control plan and quality methods
• Data ownership, retention and security requirements documented before acquisition
• Change-control procedures address weather delays, access restrictions and scope amendments
• Acceptance review uses sample checks, accuracy evidence and metadata completeness
• Project closeout archives source data, processing records and approved deliverables

Participants can diagnose common acquisition and processing failures using evidence from project data.

• Strip offsets traced to boresight error, trajectory quality or timing mismatch
• Low density investigated through speed, altitude, pulse settings and missed lines
• Vegetation artefacts separated from ground-class filtering and interpolation failures
• Datum errors diagnosed from consistent horizontal or vertical displacement patterns
• Noisy returns assessed for reflective surfaces, atmospheric conditions and range limits
• Corrective actions prioritised by deliverable risk, cost and recollection feasibility

Participants can communicate LiDAR methods, limitations and quality evidence to technical and non-technical stakeholders.

• Technical reports state objectives, acquisition dates, systems and coordinate references
• Accuracy tables distinguish tested results, tolerances and excluded checkpoint categories
• Map layouts show coverage, flight lines, control, density and quality exceptions
• Limitations explained for dense canopy, water, shadows and inaccessible terrain
• Processing assumptions documented so subsequent users can reproduce key outputs
• Client handover packages include readme files, metadata and delivery inventories

Participants can build, quality-review and present a complete LiDAR drone mapping project package.

• Project brief translated into acquisition parameters, control design and deliverables
• Flight plan justified using density calculations, terrain constraints and overlap targets
• Point-cloud workflow applies trajectory review, calibration and classification decisions
• Terrain or application product produced from validated classified point-cloud data
• Accuracy evidence compiled using checkpoints, residuals and acceptance thresholds
• Final presentation defends methods, limitations, findings and client handover recommendations