GIS and Remote Sensing Fundamentals for Spatial Data Analysis Training Course

Course overview

Organisations increasingly rely on mapped evidence to plan assets, monitor land change, assess environmental risk, target field activity and communicate location-based decisions. Yet many analysts receive spatial datasets without a reliable method for checking coordinate systems, judging data quality, combining vector layers with imagery, or explaining what a map-derived result actually means. This course addresses that gap by giving participants a disciplined foundation for turning GIS and remote-sensing data into defensible spatial analysis rather than attractive but unreliable maps.

Participants work through the GIS data model, coordinate reference systems, vector and raster operations, attribute management, spatial queries, geoprocessing and cartographic design. They learn how satellite imagery is acquired and interpreted; how to use spectral bands, vegetation indices and supervised classification; and how to assess classification accuracy. Practical sessions use QGIS, ArcGIS Pro, Google Earth Engine and GDAL to prepare data, calculate spatial relationships, detect land-cover change and document results for operational use.

Instruction combines short technical briefings with guided software demonstrations, individual lab work and a cumulative spatial-analysis case study. Participants build a repeatable workflow that moves from raw boundary, field-survey and satellite data to a clearly documented map product and decision brief. They leave with a completed spatial analysis project, including prepared datasets, processing steps, map layouts, an accuracy assessment and recommendations that can be adapted to a workplace use case.

The course is designed for professionals who already work with operational, environmental, infrastructure, planning or asset data and need practical GIS and remote-sensing capability without assuming prior specialist training in image analysis.

Course objectives

By the end of this course, participants will be able to:

• Configure project coordinate reference systems and transform spatial datasets accurately
• Prepare vector datasets through attribute joins, field calculations, topology checks and data cleaning
• Execute buffer, overlay, proximity and spatial-selection analyses to answer location-based questions
• Interpret multispectral satellite imagery using band combinations, spectral signatures and spatial resolution
• Calculate NDVI and other raster indices to identify vegetation and land-surface patterns
• Perform supervised land-cover classification and produce an error matrix for accuracy assessment
• Create publication-ready map layouts with scale, symbology, labels, legends and source metadata
• Document a reproducible GIS workflow and deliver a spatial-analysis decision brief

Benefits of attending

Target competencies

Who should attend

• GIS Analysts — who need a sound workflow for preparing, analysing and presenting spatial data
• Environmental Officers — who monitor land condition, habitats, vegetation or regulatory impacts
• Urban and Regional Planners — who assess development constraints, accessibility and land-use change
• Asset and Infrastructure Analysts — who use location data to prioritise inspections, maintenance and investment
• Surveying and Geomatics Professionals — who need to integrate field data with imagery and GIS layers
• Data Analysts — who need to add spatial queries, mapping and raster analysis to their analytical practice

Requirements and prerequisites

Participants should be comfortable using a Windows or macOS computer, managing files and folders, working with spreadsheets, and interpreting basic tables, charts and maps. Familiarity with coordinates, map scales, data types or a GIS package is helpful but not essential. The course assumes participants can follow a structured software workflow and make simple calculations; it does not assume prior programming, statistics beyond basic percentages, surveying qualifications or remote-sensing experience. Complete beginners should expect an intensive week and should review the supplied pre-course orientation on map coordinates and spatial data terminology.

Training methodology

Each day combines instructor-led explanation of spatial concepts with live demonstrations in QGIS, ArcGIS Pro and Google Earth Engine. Participants complete guided labs using boundary, asset, survey and multispectral imagery datasets, then compare methods and assumptions in small-group reviews. Case exercises require learners to select suitable data, run geoprocessing tools, interpret raster outputs and defend conclusions from maps. On the final day, participants assemble their analysis into a documented workplace-style deliverable and identify a real operational question to which the workflow can be applied.

Course outline

Day 1: GIS foundations and spatial data integrity

• GIS components, spatial questions and operational use cases
• Vector, raster and tabular data models
• Coordinate reference systems, datums and map projections
• Geographic versus projected coordinate systems
• Spatial data quality: accuracy, precision, completeness and lineage
• QGIS and ArcGIS Pro project structure and layer management
• Attribute tables, field types and relational joins

Workshop: Participants create a new GIS project, load operational boundary and asset layers, assign the correct coordinate reference system, and produce a cleaned joined dataset.

Day 2: Vector analysis and geoprocessing workflows

• Spatial queries and selection by location
• Buffer analysis for service areas and impact zones
• Clip, dissolve, merge and multipart-to-singlepart operations
• Overlay analysis using intersect, union and erase tools
• Distance, nearest-feature and proximity analysis
• Topology rules and geometry validation
• ModelBuilder and QGIS Processing Toolbox workflow design

Workshop: Participants analyse asset exposure within defined hazard and service buffers, validate geometry, and create a repeatable geoprocessing model.

Day 3: Remote sensing and raster data interpretation

• Electromagnetic spectrum and spectral reflectance
• Satellite platforms, sensors and image acquisition dates
• Spatial, spectral, radiometric and temporal resolution
• Raster properties, bands, pixels and NoData values
• True-colour, false-colour and band-composite visualisation
• Image preprocessing: clipping, resampling and cloud masking
• Google Earth Engine image collections and basic filtering

Workshop: Participants select a suitable Sentinel-2 image collection, apply date and cloud filters, create band composites, and interpret visible land-surface features.

Day 4: Raster analytics and land-cover mapping

• Raster calculator expressions and conditional analysis
• NDVI calculation and vegetation condition interpretation
• Training samples and spectral signatures
• Supervised classification using labelled reference areas
• Unsupervised classification and cluster interpretation
• Classification accuracy assessment and confusion matrices
• Change detection using multi-date imagery

Workshop: Participants classify land cover from multi-band imagery, calculate NDVI, assess the result with reference samples, and identify mapped areas of change.

Day 5: Cartographic communication and applied project delivery

• Map purpose, audience and visual hierarchy
• Symbology choices for nominal, ordinal and continuous data
• Labels, annotation, scale bars, north arrows and legends
• Map layouts for print, PDF and stakeholder briefings
• Metadata, source citation and processing documentation
• GDAL data conversion and raster format management
• Spatial-analysis recommendations and limitations statements

Workshop: Participants complete a capstone decision brief containing an analysed map layout, documented workflow, accuracy or quality notes, and recommendations for a defined operational scenario.

Tools & standards covered

QGIS, ArcGIS Pro, Google Earth Engine, GDAL

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions