GIS and Remote Sensing Professional Training Course

Participants can select, validate and document coordinate reference systems for spatial datasets.

• Geographic coordinates, projected coordinates and datum transformations compared for intended use
• EPSG codes selected from source data, geography and measurement requirements
• UTM zones, national grids and local projections assessed for distortion
• On-the-fly projection checked against authoritative reference layers and control points
• Distance, area and direction errors traced to unsuitable coordinate systems
• CRS documentation recorded in layer metadata and project specifications

Participants can create, clean and validate vector datasets for GIS analysis.

• Points, lines and polygons created from coordinates, imagery and survey records
• Topology rules applied to identify gaps, overlaps, slivers and duplicate features
• Attribute domains, subtypes and coded values designed for consistent data entry
• Geocoding addresses using reference gazetteers, match scores and exception review
• Digitising tolerances set to preserve geometry quality at intended map scales
• Editing histories and source lineage captured within dataset metadata

Participants can prepare raster datasets and derive terrain information for spatial decisions.

• Raster cell size, extent, bit depth and resampling methods selected
• Digital elevation models assessed for vertical accuracy, voids and artefacts
• Slope, aspect, hillshade and contour surfaces derived from elevation data
• Nearest neighbour, bilinear and cubic resampling matched to data types
• Raster clipping, mosaicking and reprojection performed without unintended value changes
• NoData values, masks and raster attribute tables managed consistently

Participants can structure spatial data and retrieve records using attribute and spatial queries.

• Geodatabase feature classes, tables, relationships and attachments configured
• Unique identifiers designed for joining survey, asset and administrative records
• SQL WHERE clauses filter attributes using logical, numeric and text conditions
• One-to-one and one-to-many joins checked for unmatched and duplicate records
• Field calculations standardise units, categories, dates and derived indicators
• Data dictionaries document field definitions, permitted values and update ownership

Participants can produce readable, accurate thematic maps for defined audiences and decisions.

• Visual hierarchy built through symbol size, colour, contrast and label placement
• Choropleth classification uses natural breaks, quantiles and equal intervals appropriately
• Proportional symbols, dot density and graduated colours matched to variables
• Map layouts include titles, legends, scale bars, north arrows and sources
• Colour palettes tested for accessibility, print output and categorical distinction
• Classification choices explained without overstating spatial patterns or certainty

Participants can select suitable remote sensing data based on sensor characteristics and information needs.

• Electromagnetic spectrum regions linked to land, water and vegetation properties
• Passive and active sensors compared for acquisition conditions and applications
• Spatial, spectral, temporal and radiometric resolution evaluated against requirements
• Multispectral, hyperspectral, radar and thermal imagery distinguished by capability
• Satellite scene selection considers cloud cover, seasonality, revisit frequency and cost
• Ground truth observations planned to support image interpretation and validation

Participants can prepare satellite imagery for consistent visual interpretation and quantitative analysis.

• Radiometric calibration converts digital numbers into physically interpretable reflectance values
• Atmospheric correction reduces haze, aerosol and illumination effects between scenes
• Geometric correction aligns imagery with reference layers and ground control
• Cloud, shadow and water masks remove unsuitable pixels before analysis
• Band composites highlight vegetation, built surfaces, moisture and geological features
• Image enhancement uses stretching, filtering and contrast adjustments purposefully

Participants can derive spectral indicators and classify imagery into meaningful land-cover categories.

• NDVI, NDWI and NDBI calculated for vegetation, water and built areas
• Training samples selected from representative, verified and spectrally distinct locations
• Supervised classification applies maximum likelihood and random forest methods
• Unsupervised clustering groups pixels before analyst interpretation and labelling
• Post-classification smoothing removes isolated pixels while preserving meaningful boundaries
• Land-cover classes defined using observable criteria and documented class definitions

Participants can assess classification reliability and identify meaningful spatial change between dates.

• Confusion matrices compare classified outputs against independent reference observations
• Producer's accuracy, user's accuracy and overall accuracy interpreted correctly
• Kappa statistics used cautiously alongside class-specific error measures
• Post-classification comparison identifies land-cover transitions between validated maps
• Image differencing detects change while controlling seasonal and sensor variation
• Change results reported with uncertainty, thresholds and verification requirements

Participants can apply core spatial analysis methods to answer operational and planning questions.

• Buffers model service areas, exclusion zones, exposure distances and catchments
• Spatial joins summarise incidents, population and assets within administrative areas
• Intersect, union and erase operations combine constraints from multiple polygon layers
• Kernel density estimation identifies concentrations of events, demand or risk
• IDW and kriging interpolate continuous surfaces from sampled observations
• Analysis parameters documented to support review, replication and sensitivity testing

Participants can build network and multi-criteria models for access, allocation and site selection.

• Network datasets represent roads, paths, restrictions, travel times and turn costs
• Service areas calculate reachable locations within defined travel-time thresholds
• Closest facility analysis assigns demand points to available service locations
• Weighted overlay combines standardised criteria using transparent suitability scores
• Analytic Hierarchy Process structures criteria weighting and stakeholder comparisons
• Sensitivity analysis tests how weighting assumptions affect candidate site rankings

Participants can plan field data collection and integrate GNSS observations into GIS datasets.

• Field forms specify attributes, validation rules, photographs and mandatory observations
• GNSS accuracy, precision, multipath and differential correction assessed before collection
• Sampling designs use random, systematic, stratified and purposive selection methods
• Offline field workflows manage basemaps, synchronisation, permissions and device security
• Collected coordinates checked against imagery, reference layers and field notes
• Chain-of-custody records preserve observation provenance and accountability

Participants can prepare GIS outputs for sharing and identify opportunities for workflow automation.

• Web map layers configured with scale ranges, pop-ups and controlled symbology
• OGC WMS, WFS and WMTS services distinguished by content and delivery method
• Tile caches and feature services selected for performance and update requirements
• ModelBuilder and graphical workflows automate repeatable geoprocessing sequences
• Python scripting concepts identify inputs, outputs, parameters and error handling
• Access permissions protect sensitive locations, personal data and operational records

Participants can apply quality controls and governance practices to GIS and remote sensing outputs.

• Positional, attribute, temporal and logical consistency quality dimensions assessed
• Acceptance criteria define tolerances for completeness, accuracy and timeliness
• ISO 19115 metadata elements record provenance, constraints, quality and contacts
• Peer review checklists test projection, symbology, analysis logic and interpretation
• Version control procedures manage edits, approvals, archived layers and rollback
• Data protection assessments identify risks from location, imagery and personal records

Participants can communicate spatial evidence, uncertainty and recommendations to decision-makers.

• Decision questions translated into maps, indicators, scenarios and recommendation statements
• Map annotations highlight findings without obscuring context, uncertainty or exceptions
• Dashboard metrics combine spatial summaries with operational performance indicators
• Methods statements explain sources, assumptions, processing steps and limitations
• Uncertainty communicated through confidence classes, ranges and qualifying language
• Briefing structures connect geographic findings to actions, owners and deadlines

Participants can build, review and present a complete GIS and remote sensing project addressing a defined decision problem.

• Project brief defines decision context, stakeholders, geography, constraints and deliverables
• Source datasets assessed for fitness, provenance, coordinate systems and licensing
• Vector, raster and imagery workflows assembled into a documented analysis sequence
• Spatial model and classification outputs reviewed against evidence and assumptions
• Final maps, metadata, quality checks and recommendations prepared for decision-makers
• Peer review tests reproducibility, cartographic clarity, analytical validity and presentation