Drone and GIS Analytics Training Course

Participants can select and apply coordinate systems suitable for drone-derived spatial data.

• Geographic coordinates, projected coordinates, datums and vertical reference systems
• Selecting coordinate reference systems using location, purpose and measurement needs
• EPSG codes and metadata used to identify spatial reference definitions
• Reprojecting vector and raster layers without introducing measurement errors
• Horizontal and vertical accuracy expressed through RMSE and confidence intervals
• Scale, ground sampling distance and minimum mapping unit relationships

Participants can identify governance controls required before collecting drone data.

• Airspace classification, operational permissions and local aviation authority requirements
• SORA principles for operational risk assessment and mitigation planning
• Pre-flight risk registers covering people, property, weather and obstacles
• Privacy impact assessment for imagery containing identifiable persons or premises
• Data classification, retention schedules and access-control requirements
• Incident reporting procedures for unsafe flights, near misses and data breaches

Participants can design a flight plan that meets coverage, resolution and overlap requirements.

• Translating client questions into area, accuracy, resolution and deliverable specifications
• Flight altitude calculated from sensor dimensions and required ground sampling distance
• Frontlap and sidelap settings for reliable photogrammetric image matching
• Grid, double-grid, corridor and oblique mission patterns compared
• Terrain-following flight plans for variable elevation and obstruction clearance
• Weather thresholds affecting image blur, shadows, wind and mission safety

Participants can select drone platforms and sensors for defined survey requirements.

• Multirotor and fixed-wing platforms matched to site size and endurance
• RGB sensors for mapping, inspection evidence and visual interpretation
• Multispectral bands used for vegetation indices and crop condition assessment
• Thermal sensors applied to heat loss, water stress and equipment anomalies
• LiDAR point clouds compared with image-derived surface models under canopy
• RTK and PPK positioning methods for improved camera location accuracy

Participants can plan ground control and field observations that support accurate outputs.

• Ground control points positioned for edge coverage and elevation variation
• Checkpoints separated from control points for independent accuracy assessment
• Survey-grade GNSS observations recorded with coordinate and datum metadata
• Target design, visibility and distribution for aerial image identification
• Field attribute forms structured for assets, observations and photo references
• Chain-of-custody records for field files, images and survey observations

Participants can assess whether captured imagery is fit for photogrammetric processing.

• EXIF metadata reviewed for timestamp, focal length, location and camera settings
• Motion blur, poor focus and exposure errors identified systematically
• Image overlap checked against planned frontlap and sidelap thresholds
• Missing coverage, shadow zones and occlusions mapped before processing
• Camera calibration indicators examined for lens distortion and focal stability
• Field logs reconciled with image folders, flight tracks and control records

Participants can process aerial imagery through a documented photogrammetry workflow.

• Structure from Motion processing stages from alignment through dense reconstruction
• Camera alignment evaluated using tie points and reprojection error
• Ground control coordinates imported and marked within aerial image sets
• Bundle adjustment refined using control residuals and camera parameters
• Dense point clouds filtered for noise, outliers and unwanted objects
• Processing reports retained as evidence of methods and quality results

Participants can generate and interpret standard drone-derived geospatial products.

• Orthomosaic generation corrected for relief displacement and camera geometry
• Digital surface models representing terrain, vegetation, structures and vehicles
• Digital terrain models derived through ground-point classification procedures
• Canopy height models calculated by subtracting terrain from surface elevations
• Point-cloud attributes including intensity, classification, RGB and elevation values
• Raster export settings selected for resolution, compression and interoperability

Participants can build a structured GIS project for drone data analysis and reporting.

• Project folders organised for raw data, processing, analysis and published outputs
• Geodatabases designed with feature classes, domains and attribute validation
• Layer naming conventions supporting traceability, collaboration and version control
• Metadata records documenting lineage, coordinate systems, dates and limitations
• Spatial joins connecting field observations with drone-derived map features
• Symbology standards applied consistently across analytical maps and layouts

Participants can create and attribute operational features from drone imagery.

• Heads-up digitising used to capture assets, boundaries and observed defects
• Feature templates standardised for roads, roofs, drains, poles and structures
• Topology rules identify gaps, overlaps, duplicates and invalid boundary geometry
• Attribute schemas record condition, inspection date, priority and evidence links
• Linear referencing supports chainage-based corridor inspection and maintenance reporting
• Photo attachments linked to mapped features for field verification

Participants can derive terrain, drainage and volumetric measures from drone data.

• Hillshade, slope and aspect derived from digital elevation models
• Flow direction and accumulation identify drainage paths and ponding risks
• Cut-and-fill volumes calculated against design surfaces or baseline terrain
• Raster calculator expressions combine elevation, vegetation and land-cover layers
• Zonal statistics summarise drone-derived values within management polygons
• Contour generation configured using suitable intervals and elevation smoothing

Participants can classify drone imagery and calculate vegetation condition indicators.

• Training samples selected for supervised classification of land-cover classes
• Random Forest classification applied to multispectral imagery and derived indices
• NDVI calculated from near-infrared and red reflectance band values
• Object-based image analysis separates vegetation patches using image segments
• Threshold classification identifies stressed vegetation, bare soil and standing water
• Class labels verified against field observations and high-resolution imagery

Participants can evaluate positional, elevation and classification accuracy using documented evidence.

• Checkpoint residuals used to calculate horizontal and vertical RMSE values
• Accuracy statements reported with confidence level, sample size and datum
• Confusion matrices compare classified outputs against independent reference samples
• Producer's accuracy, user's accuracy and kappa interpreted for mapped classes
• Acceptance criteria defined before delivery for spatial and thematic products
• Quality assurance checklists record corrections, approvals and residual risks

Participants can identify and quantify meaningful changes between drone survey dates.

• Co-registration checks ensure comparable alignment between repeat survey datasets
• Image differencing highlights changes in reflectance, elevation and surface condition
• DEM of Difference quantifies erosion, stockpile movement and construction progress
• Temporal attribute tables track inspection findings, condition scores and interventions
• Change thresholds distinguish material changes from sensor noise and seasonal variation
• Before-and-after maps communicate location, magnitude and confidence of change

Participants can create 3D representations and measurements for asset and site analysis.

• Textured mesh models generated for structures, facades and complex terrain
• Point-cloud clipping isolates structures, stockpiles and inspection target areas
• Cross-sections measure embankments, channels, excavations and slope profiles
• Three-dimensional distance measurements account for elevation and surface geometry
• Viewshed analysis evaluates line-of-sight constraints around proposed infrastructure
• 3D scenes configured with elevation exaggeration, labels and inspection viewpoints

Participants can produce maps and reports that support accountable operational decisions.

• Map layouts designed with hierarchy, scale bars, north arrows and source notes
• Operational dashboards present inspection status, priority locations and trend indicators
• Technical reports document methods, accuracy, assumptions and analytical limitations
• Issue maps distinguish observed facts, interpreted conditions and recommended actions
• Web map publishing options assessed for access control and information sensitivity
• Briefing narratives connect spatial evidence with costs, risks and next decisions

Participants can standardise recurring drone and GIS analysis tasks for repeat delivery.

• ModelBuilder workflows sequence geoprocessing tools for repeatable analytical outputs
• Python geoprocessing concepts automate file naming, clipping and batch calculations
• Processing parameters recorded in templates for consistent project delivery
• Data validation rules identify missing attributes, invalid geometry and projection conflicts
• Versioned outputs retain dates, analysts, approvals and processing status
• Standard operating procedures define handoffs between field, processing and GIS teams

Participants can commission and evaluate drone data services against technical requirements.

• Terms of reference specify survey extent, resolution, accuracy and deliverable formats
• Supplier proposals assessed against safety, methodology, capacity and quality evidence
• Acceptance testing checks coverage, coordinate accuracy, completeness and metadata
• Service-level requirements define turnaround times, issue handling and data ownership
• Cost drivers include site access, flight complexity, processing and reporting effort
• Procurement records retain permissions, insurance, quality reports and final datasets

Participants can prepare a scoped implementation plan for a drone and GIS use case.

• Use-case canvases define stakeholders, decisions, inputs, outputs and success measures
• Data requirements matrices link questions with sensors, control and GIS layers
• Workflow diagrams assign responsibilities, approvals, dependencies and quality gates
• Implementation risks assessed for regulation, safety, skills, data and procurement
• Pilot project scope balances operational value, feasibility and available resources
• Benefits measures track time saved, coverage improved and errors prevented

Participants can build, review and present a complete drone and GIS analytical product for an operational decision.

• Survey brief converted into flight, control, processing and analysis specifications
• Drone imagery processed into documented orthomosaic, elevation and point-cloud outputs
• GIS analysis developed using extracted features, raster measures and spatial queries
• Accuracy evidence compiled from checkpoints, classification samples and quality checks
• Decision map and technical report prepared for a defined stakeholder audience
• Peer review tests assumptions, limitations, traceability and recommended operational actions