Geospatial Intelligence Systems Training Course

Participants can produce a collection plan linked to priority intelligence requirements and indicators.

• Requirements matrix linking decisions, questions, indicators and collection needs
• Collection plans specify source, timing, resolution, access and limitations
• Named areas of interest defined using operational boundaries
• Indicator lists distinguish normal activity from reportable change
• Collection gaps prioritised using impact, urgency and source availability
• Requests for information written with measurable geospatial specifications

Participants can select appropriate spatial data models and assess dataset fitness for purpose.

• Vector geometry types: points, lines and polygons
• Raster cells, bands, resolution and pixel values explained
• Attribute tables structured using unique identifiers and controlled values
• Data quality assessed through accuracy, completeness, currency and consistency
• Positional uncertainty recorded for field observations and derived features
• Fitness-for-purpose decisions documented against analytical requirements

Participants can manage coordinate reference systems and georeference source material accurately.

• Geographic and projected coordinate systems compared for analytical use
• Datum differences identified before combining source datasets
• UTM zones selected for local measurement and mapping
• Coordinate transformations applied using documented transformation parameters
• Georeferencing control points selected from stable, identifiable features
• Root mean square error reviewed before accepting georeferenced imagery

Participants can acquire, catalogue and assess geospatial sources using metadata and provenance controls.

• Authoritative, commercial and open geospatial sources evaluated
• Metadata records capture lineage, scale, date, licence and restrictions
• Data registers assign owners, versions, classification and update cycles
• Open-source geolocation checked through corroboration and temporal verification
• File formats compared: GeoPackage, GeoJSON, GeoTIFF and shapefile
• Naming conventions support reproducible datasets and analytical outputs

Participants can prepare analysis-ready datasets through structured geoprocessing workflows.

• Clipping datasets to areas of interest and reporting boundaries
• Dissolve operations aggregate features using defined attribute criteria
• Spatial joins transfer attributes using documented relationship rules
• Geocoding addresses checks match rates and unmatched records
• Topology rules identify gaps, overlaps, duplicates and invalid geometry
• Processing logs record inputs, parameters, outputs and quality checks

Participants can create readable intelligence maps that support rapid and accurate interpretation.

• Visual hierarchy directs attention to operationally significant information
• Symbol classes match data type, scale and decision purpose
• Classification methods compared: natural breaks, quantiles and equal intervals
• Map marginalia includes projection, sources, date, scale and caveats
• Colour choices distinguish categories without obscuring uncertainty
• Layout reviews test legibility at briefing and print sizes

Participants can explain how sensors, spectral behaviour and resolution affect imagery use.

• Electromagnetic spectrum regions used by common earth observation sensors
• Spectral signatures distinguish vegetation, water, soil and built surfaces
• Spatial, spectral, temporal and radiometric resolution compared
• Passive and active sensors selected for collection conditions
• Atmospheric effects identified in optical satellite imagery
• Sensor metadata assessed before imagery interpretation begins

Participants can identify features and assess activity using structured visual imagery interpretation.

• Image interpretation elements: tone, texture, shape, shadow and pattern
• Site function inferred from structures, access routes and equipment
• Activity indicators identified through vehicles, stockpiles and disturbed ground
• Oblique and nadir imagery compared for feature visibility
• Interpretation keys document observable features and confidence levels
• False positives reduced through contextual and multi-source checks

Participants can prepare imagery and apply basic classification methods for thematic analysis.

• Radiometric correction improves comparison between image acquisition dates
• Band combinations selected for vegetation, water and urban interpretation
• Image enhancement uses contrast stretching and histogram analysis
• Supervised classification built from representative training samples
• Unsupervised clustering reviewed against known land-cover categories
• Confusion matrices report classification accuracy and error patterns

Participants can derive terrain intelligence products from digital elevation models.

• Digital elevation models assessed for resolution, source and voids
• Slope and aspect surfaces derived for movement assessment
• Hillshade and contour products reveal terrain form
• Viewshed analysis estimates observation coverage from selected locations
• Least-cost paths model movement using terrain friction surfaces
• Terrain constraints linked to access, exposure and route planning

Participants can analyse proximity, service access and movement routes using spatial relationships.

• Buffer analysis identifies exposure zones around hazardous assets
• Euclidean distance surfaces measure proximity without network constraints
• Network datasets model roads, restrictions, speeds and turn costs
• Service areas estimate reachable locations within stated travel times
• Origin-destination matrices compare routes, times and distances
• Accessibility findings tested against road status and seasonal conditions

Participants can identify spatial patterns and communicate their analytical significance carefully.

• Point pattern maps distinguish concentration from reporting density
• Kernel density estimation identifies relative event concentration areas
• Hot spot analysis applies Getis-Ord Gi-star statistics
• Spatial autocorrelation tested using Global Moran's I
• Rate calculations normalise incidents against population or exposure
• Pattern findings separated from causal claims and unsupported inference

Participants can detect, validate and report meaningful change across geospatial time series.

• Multi-date imagery aligned before pixel-based change assessment
• Image differencing identifies candidate changes between acquisition dates
• Normalized Difference Vegetation Index tracks vegetation condition changes
• Feature comparison identifies construction, damage and land-use change
• Temporal baselines separate routine variation from anomalous activity
• Change reports state dates, evidence, confidence and alternative explanations

Participants can build transparent weighted-overlay models for suitability and risk assessment.

• Decision criteria translated into measurable spatial variables
• Standardisation converts different measurement scales into comparable scores
• Weighted overlay combines factors using documented relative importance
• Constraint layers exclude unsuitable, restricted or inaccessible areas
• Sensitivity analysis tests effects of changed weights and thresholds
• Model outputs validated against observations, records or expert review

Participants can write geospatial assessments that distinguish observations, judgements and confidence.

• Intelligence assessments separate facts, assumptions, judgements and forecasts
• Source reliability and information credibility assessed using Admiralty Code
• Confidence statements reflect corroboration, source quality and analytical limits
• Alternative hypotheses tested through Analysis of Competing Hypotheses
• Key judgements written before supporting technical detail
• Map annotations connect evidence directly to assessed findings

Participants can apply governance controls to sensitive geospatial data and intelligence products.

• Data classification determines handling, storage and dissemination controls
• Personally identifiable location data assessed for privacy risks
• Minimum necessary data principles guide collection and retention
• Sensitive site coordinates protected through aggregation or masking
• Licensing terms checked before reuse, publication and distribution
• Audit trails preserve analytical decisions, source lineage and approvals

Participants can select appropriate dissemination formats for operational users and decision-makers.

• Web maps configured with scale-dependent layers and controlled visibility
• Dashboards combine indicators, maps and operational status summaries
• Briefing maps prioritise decisions, deadlines and required actions
• Static products used where connectivity, security or printing constrain access
• User access roles protect sensitive layers and analytical notes
• Dissemination plans define recipients, timing, format and feedback channels

Participants can manage an end-to-end workflow for a time-sensitive geospatial intelligence task.

• Task triage ranks incoming requirements by urgency and consequence
• Workflow boards track collection, processing, analysis and review status
• Quality assurance checks occur before release of intelligence products
• Peer review challenges assumptions, methods and map interpretation
• Version control distinguishes draft, reviewed and approved outputs
• After-action reviews capture lessons for future collection cycles

Participants can build, review and present a complete geospatial intelligence assessment for a defined operational decision.

• Intelligence requirement framed with decision, indicators and operational context
• Source register documents imagery, vector data, metadata and limitations
• Analytical workflow combines terrain, proximity, change and risk methods
• Map series communicates baseline conditions, findings and priority areas
• Written assessment states key judgements, confidence and recommendations
• Peer review tests evidence chains, caveats and decision relevance