GIS and Remote Sensing for Monitoring and Evaluation Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Monitoring and Evaluation Officers, Programme Managers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-ME-076

Before you join: Participants should understand basic monitoring and evaluation concepts, including indicators, baselines, targets, data sources and disaggregation.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Monitoring and evaluation teams often hold survey tables, facility registers, beneficiary records and narrative reports that cannot show where results are occurring, who remains unreached, or whether observed change is linked to programme activity. GIS and remote sensing add location, distance, land cover, population and time-series evidence to the M&E process. Used well, they improve baseline design, targeting, field verification, coverage analysis, equity analysis, damage assessment and reporting to decision makers.

This course teaches participants to build defensible spatial evidence for development and humanitarian programmes. Participants work with GIS data models, coordinate systems, mobile data collection, data quality checks, cartographic design and spatial analysis. They use satellite imagery to assess land cover, vegetation, water, flood extent and change over time, then test map accuracy and state uncertainty clearly. The course connects these methods to results chains, indicator reference sheets, sampling plans, baseline maps, service coverage and response monitoring.

Teaching combines instructor briefings, guided software demonstrations, sector cases, group analysis and individual application planning. Cases reflect common work in health, food security, livelihoods, education, WASH, disaster response and protection. Participants progressively assemble a spatial M&E work package using a realistic programme scenario, then refine it through review. They leave with reusable templates and a completed GIS and remote sensing analysis package containing mapped indicators, documented methods, quality checks and an evidence brief for management use.

The course suits M&E professionals, programme managers, analysts and technical officers who already work with programme data and need to turn it into location-based evidence. It is also appropriate for GIS staff who need stronger understanding of results measurement, indicator quality and evaluation use.

What you will be able to do

  • Design spatial indicators linked to results chains, indicator reference sheets and geographic units
  • Prepare GIS datasets using coordinate systems, metadata, joins, topology checks and quality logs
  • Produce baseline, coverage, equity and accessibility maps using appropriate cartographic methods
  • Configure georeferenced mobile data collection workflows with KoboToolbox or ODK Collect
  • Process satellite imagery using reflectance products, cloud masks, band composites and raster alignment
  • Apply land-cover classification, spectral indices and change-detection methods to M&E questions
  • Validate spatial outputs using reference samples, confusion matrices and uncertainty statements
  • Present a documented GIS and remote sensing M&E work package for management decisions

Benefits of attending

For you

  • Ability to convert routine M&E data into decision-ready geographic evidence
  • Practical experience producing mapped baselines, coverage analyses and change assessments
  • Stronger capability to question map accuracy, uncertainty and inappropriate spatial claims
  • Reusable methods for designing georeferenced surveys and field verification protocols
  • A portfolio-quality GIS and remote sensing M&E work package

For your organisation

  • Better geographic targeting of services, assessments and field monitoring resources
  • More credible baseline and progress reporting with documented spatial methods
  • Earlier identification of coverage gaps, excluded populations and emerging risks
  • Improved consistency in map production, metadata, validation and data protection
  • Clearer evidence briefs linking spatial findings to management actions and budget decisions

Course modules

20 modules over 10 days

Participants can frame M&E questions that require geographic and remote-sensing evidence.

  • Results chains connect activities, outputs, outcomes, impacts and spatial evidence
  • Indicator reference sheets specify unit, denominator, frequency and disaggregation
  • Geographic questions distinguish location, distribution, proximity, change and exposure
  • Spatial indicators align administrative boundaries with programme target populations
  • Baseline, monitoring and evaluation decisions require different spatial evidence
  • Ethical data principles address consent, protection, representation and potential harm

Participants can select suitable spatial data structures and reference systems for programme analysis.

  • Vector features represent points, lines, polygons and associated attributes
  • Raster cells store continuous surfaces, categorical classes and temporal observations
  • Coordinate reference systems prevent displacement between datasets and field locations
  • Attribute tables link beneficiary records to mapped geographic features
  • Scale, resolution, extent and accuracy determine fitness for purpose
  • Metadata records source, date, methods, limitations, ownership and permitted use

Participants can establish a controlled GIS project structure for repeatable M&E analysis.

  • QGIS project structure separates raw data, processed layers, outputs and documentation
  • Layer symbology, labels and filters reveal indicator patterns without distorting values
  • Geoprocessing history records parameters, inputs and outputs for repeatable analysis
  • File geodatabases, GeoPackages and cloud folders support controlled team data management
  • Importing CSV, Excel and shapefile data preserves field types and identifiers
  • Project templates establish coordinate systems, naming rules and output conventions

Participants can plan and quality-control collection of georeferenced field observations.

  • GNSS accuracy, satellite geometry and multipath effects influence recorded field positions
  • Waypoint, track and polygon capture match facilities, routes and community boundaries
  • Offline mobile forms capture coordinates, photographs, timestamps and enumerator identifiers
  • Coordinate transformations reconcile WGS 84 observations with national mapping grids
  • Field protocols define minimum accuracy thresholds, revisit rules and exception handling
  • Photo geotagging creates auditable links between observations and mapped assets

Participants can prepare programme datasets for reliable spatial analysis and reporting.

  • Unique identifiers join survey records, facility registers and administrative boundary layers
  • Data cleaning detects duplicates, missing coordinates, invalid dates and impossible values
  • Spatial joins assign households, sites and events to administrative units
  • Geocoding procedures standardise place names before matching incomplete address records
  • Topology checks identify polygon gaps, overlaps, slivers and disconnected boundaries
  • Quality logs document corrections, unresolved issues and dataset acceptance decisions

Participants can produce geographic baselines and define spatially meaningful programme indicators.

  • Baseline maps combine indicator values, population denominators and programme catchments
  • Disaggregated indicators reveal differences by sex, age, disability and location
  • Target-setting uses baseline values, planned coverage and realistic geographic reach
  • Administrative boundary versions preserve comparability between reporting periods
  • Small-area profiles identify priority communities beneath district-level averages
  • Indicator maps distinguish counts, rates, proportions and index values

Participants can create readable maps that communicate findings without misleading users.

  • Choropleth classification uses quantiles, natural breaks and equal intervals appropriately
  • Proportional symbols communicate totals without implying equal-area population distribution
  • Colour schemes meet accessibility requirements and preserve ordered data meaning
  • Map layouts include title, legend, scale, source, date and caveats
  • Annotation separates observed findings from operational interpretation and recommended action
  • Export settings produce readable PDF, PNG and print-ready map products

Participants can design georeferenced survey workflows with appropriate sampling and protection controls.

  • Sampling frames use mapped settlements, facilities and population grids
  • Spatial sampling applies random, systematic, stratified and cluster selection methods
  • Survey form logic reduces inconsistent responses and records skip patterns
  • KoboToolbox and ODK Collect support offline georeferenced field submissions
  • Enumerator monitoring maps submission locations, duration anomalies and incomplete records
  • Data protection procedures secure devices, transmission, access roles and retention

Participants can judge whether satellite data can answer a defined M&E question.

  • Electromagnetic spectrum bands explain visible, near-infrared and thermal observations
  • Spatial, spectral, temporal and radiometric resolution shape analytic suitability
  • Sentinel-2 and Landsat missions provide repeat observations for programme areas
  • Cloud cover, shadows and seasonal timing affect image selection
  • Passive and active sensors produce different evidence for monitoring questions
  • Remote sensing limitations require ground evidence before operational conclusions

Participants can prepare satellite imagery for defensible comparison and analysis.

  • Image catalogues filter scenes by date, cloud cover and footprint
  • Surface reflectance products reduce atmospheric effects before multi-date comparison
  • Cloud and shadow masks remove unreliable pixels from analysis
  • Band composites highlight vegetation, water, built-up areas and bare soil
  • Clipping rasters to areas of interest reduces processing volume
  • Raster alignment maintains matching extent, resolution and pixel origin

Participants can classify land cover using documented visual and spectral interpretation methods.

  • Visual interpretation uses tone, texture, pattern, shape and context
  • Training samples represent classes across seasons, sensors and geographic conditions
  • Supervised classification distinguishes predefined land-cover categories using reference samples
  • Unsupervised clustering groups spectrally similar pixels before analyst labelling
  • Object-based interpretation improves mapping where pixel resolution permits segmentation
  • Class definitions document inclusion rules, exclusions and minimum mapping units

Participants can apply spectral indices and change methods to programme monitoring questions.

  • NDVI measures vegetation greenness from red and near-infrared reflectance
  • NDWI identifies surface water using green and near-infrared bands
  • Burn severity indices compare pre-event and post-event spectral conditions
  • Image differencing detects change after consistent preprocessing and seasonal matching
  • Time-series charts show trends, anomalies and recovery across observation dates
  • Threshold selection links spectral change to clearly documented programme questions

Participants can assess map reliability and communicate uncertainty to evidence users.

  • Reference samples separate training, validation and independent verification datasets
  • Confusion matrices calculate producer accuracy, user accuracy and overall accuracy
  • Precision, recall and F1 scores assess class-specific mapping performance
  • Positional accuracy checks compare mapped features with trusted reference locations
  • Uncertainty statements prevent false precision in maps and indicator estimates
  • Validation reports state sample design, results, limitations and intended use

Participants can identify underserved, exposed and high-priority areas using spatial analysis.

  • Buffer analysis estimates populations within defined distances of programme assets
  • Network analysis calculates travel time using roads, paths, barriers and speeds
  • Cost-distance surfaces model access where road networks are incomplete
  • Overlay methods identify communities exposed to floods, drought or conflict events
  • Population grids estimate affected people when household registers are unavailable
  • Multi-criteria ranking combines need, access, hazard and programme feasibility

Participants can analyse geographic coverage and equity patterns in programme results.

  • Coverage ratios compare services delivered with eligible population denominators
  • Catchment analysis tests whether facility use matches intended service areas
  • Equity analysis compares indicator outcomes across wealth, gender and location
  • Hotspot analysis identifies statistically unusual clusters requiring investigation
  • Spatial autocorrelation tests whether observed patterns exceed random variation
  • Before-and-after maps separate programme signals from contextual change cautiously

Participants can create map-based reporting products for operational and management audiences.

  • Indicator dashboards connect mapped layers, charts, filters and reporting periods
  • Data refresh schedules define responsible owners, checks and publication deadlines
  • Drill-down views move from national summaries to community-level evidence
  • Decision briefs pair maps with findings, caveats and proposed actions
  • Audience-specific products distinguish operational, management, donor and community needs
  • Version control prevents conflicting maps and outdated indicator releases

Participants can plan geographically sound samples and interpret small-area estimates responsibly.

  • Spatial sampling plans document target population, frame, selection and replacement rules
  • Area-based sampling uses gridded cells where household lists are absent
  • Sample allocation balances precision, cost, accessibility and subgroup reporting needs
  • Spatial weights adjust estimates for unequal selection probabilities and nonresponse
  • Small-area estimation combines survey data with auxiliary geographic predictors
  • Survey map products show sampled locations without exposing respondent identities

Participants can apply remote-sensing evidence appropriately during humanitarian assessment and response.

  • Rapid mapping priorities distinguish situational awareness, damage assessment and response tracking
  • Pre-event baselines support comparisons after floods, earthquakes and displacement
  • Satellite-derived flood extent requires cloud-aware imagery and ground confirmation
  • Damage proxy mapping identifies affected structures but does not confirm needs
  • Humanitarian data standards govern sharing, sensitivity classification and access restrictions
  • Response maps track assessed sites, assistance reach and remaining gaps

Participants can establish governance controls for secure, traceable and repeatable spatial analysis.

  • Data management plans assign stewardship, storage, backups, access and archival
  • FAIR principles improve findability, accessibility, interoperability and reuse of datasets
  • Metadata profiles record lineage, processing steps, coordinate systems and quality
  • Role-based access protects beneficiary locations and sensitive protection information
  • Reproducible workflows store scripts, parameters, source files and output versions
  • Disclosure controls aggregate or mask locations before public map release

Participants can build, review and present a complete GIS and remote sensing M&E evidence package for a programme decision.

  • Problem statement defines decision users, geographic scope and M&E questions
  • Indicator matrix specifies sources, methods, disaggregation, timing and quality criteria
  • Processing workflow documents GIS operations, remote-sensing methods and validation checks
  • Final maps communicate baseline, change, coverage, equity or exposure findings
  • Evidence brief states limitations, ethical safeguards and recommended management actions
  • Peer review tests traceability, map readability and decision relevance

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Spatial M&E indicator reference sheet template
  2. GIS project structure and metadata checklist
  3. Georeferenced survey and field verification protocol
  4. Satellite image preprocessing and validation workflow
  5. Coverage, equity and accessibility analysis model
  6. GIS and remote sensing evidence brief template

Who should attend

  • Monitoring and Evaluation Officers — need location-based evidence for indicators, baselines and reporting
  • Programme Managers — need maps to target resources, assess coverage and direct corrective action
  • GIS Analysts — need to connect spatial methods with results frameworks and evaluation questions
  • Humanitarian Information Managers — need rapid, defensible maps for assessments and response tracking
  • Research and Data Analysts — need spatial sampling, validation and small-area analysis methods
  • Sector Technical Advisers — need geographic evidence for health, WASH, education or livelihoods decisions

Skills and tools covered

Spatial indicator designGIS data managementSatellite image analysisCoverage equity analysisMap-based reportingSpatial data quality

Tools and standards

QGIS, KoboToolbox, ODK Collect, FAIR Principles

How the course runs

Training methodology

The course uses short instructor briefings followed by guided software demonstrations, case analysis, group exercises and individual application work. Participants work through development and humanitarian scenarios involving facility coverage, household surveys, flood impacts, land-use change and service access. They build maps and analyses in stages, receive structured peer review, and test interpretations against indicator definitions and data-quality requirements. Simulations focus on decisions such as targeting field teams, prioritising communities and reporting evidence under uncertainty. Each participant prepares an application plan for a current or planned M&E assignment.

Requirements and prerequisites

Participants should understand basic monitoring and evaluation concepts, including indicators, baselines, targets, data sources and disaggregation. Experience using spreadsheets to review programme data is expected. Participants should be comfortable working with tables, filtering records and interpreting simple charts. Prior use of GIS software is helpful but not required; the course starts with spatial data structures, coordinate systems and project setup. Coding, advanced statistics, prior remote sensing analysis and specialist cartography experience are not required. Participants should bring examples of non-sensitive programme questions or indicator definitions where possible.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

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

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants should understand basic M&E concepts and be comfortable using spreadsheets. Previous GIS, coding and remote sensing experience are not required, although participants with existing GIS experience will deepen their analytical and reporting methods.

A laptop is useful for software-based work, together with non-sensitive indicator definitions, maps or programme questions where available. Training datasets and course materials are provided, and participants should not bring beneficiary-level data without their employer's approval.

The course is designed for M&E officers, programme managers, GIS analysts, information managers, researchers and sector advisers. It is particularly relevant to staff supporting health, WASH, education, livelihoods, food security, protection and humanitarian response programmes.

General GIS courses often focus on software functions, while general remote sensing courses may focus on image science. This course connects both disciplines directly to results frameworks, indicators, baselines, coverage, equity, evaluation and programme decisions.

Participants can use the templates and workflow to map baseline conditions, monitor service reach, target fieldwork, assess geographic equity and report spatial evidence. The capstone provides a model for structuring a real programme analysis with documented assumptions and quality checks.

The first part builds core GIS, data preparation, mobile collection and map design skills while participants begin their work package. The second part covers satellite analysis, validation, accessibility, equity, humanitarian applications, governance and the final capstone presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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