Select imagery sources and resolution trade-offs that match a monitoring or planning objective.
Digital Transformation & Artificial Intelligence
Geospatial AI and Satellite Imagery Analysis for Monitoring and Planning
Apply geospatial AI to satellite and aerial imagery for environmental monitoring, agriculture, infrastructure planning and disaster response operations.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Satellite imagery now arrives faster than any team could review it by eye, and the gap between a useful monitoring system and a folder of unreviewed scenes is entirely the modelling and validation pipeline built behind it. This course works from the ground up: choosing between optical, multispectral and radar imagery for a given monitoring condition, preparing raster and vector data consistently, and training classification, segmentation and object detection models on satellite and aerial scenes. Change detection and time-series analysis track deforestation, crop health and urban growth across multi-date imagery, and the same techniques are redirected towards disaster response, mapping flood extent and structural damage from post-event scenes within hours rather than weeks. Validation against field-surveyed ground truth runs throughout, because a classification map used for carbon credit verification or enforcement action has to survive scrutiny, not just look convincing. The last module addresses the data sovereignty and privacy questions that high-resolution imagery raises before any of this reaches routine operational use.
Expected Learning Outcomes
Prepare raster and vector geospatial data consistently for machine learning pipelines.
Train classification, segmentation and object detection models on satellite and aerial imagery.
Detect land cover change and track vegetation indices across multi-date image time series.
Apply geospatial AI to environmental monitoring, agriculture, infrastructure and disaster response.
Validate model accuracy against field-surveyed ground truth before operational use.
Address data sovereignty, privacy and update-frequency requirements in an operational pipeline.
Who Should Attend
Remote sensing analysts and GIS specialists adopting machine learning for imagery analysis.
Environmental and agricultural agencies monitoring land use, crops or deforestation.
Urban and infrastructure planners using satellite data for site selection and impact assessment.
Disaster response teams needing rapid damage and flood extent assessment from imagery.
Sustainability teams estimating and reporting carbon stock changes for climate disclosures.
Data scientists extending computer vision skills into geospatial and earth observation data.
Course Modules
Select any module to see its sessions and points.
01Satellite and Aerial Data for Geospatial AI
2 sessions · 8 points
Session 1Imagery Sources and Resolution Trade-offs
- Comparing optical, multispectral and synthetic aperture radar imagery for different monitoring conditions.
- Weighing spatial, temporal and spectral resolution trade-offs against a monitoring or planning objective.
- Selecting between public archive imagery and higher-resolution commercial tasking for a project's accuracy needs.
- Applying cloud masking and atmospheric correction before imagery is fit for quantitative analysis.
Session 2Preparing Geospatial Data for Modelling
- Managing raster and vector data formats and coordinate reference systems consistently across a pipeline.
- Tiling large satellite scenes into training-ready patches without losing context at tile edges.
- Aligning multi-date imagery precisely enough to support reliable change detection.
- Following Open Geospatial Consortium data standards to keep outputs interoperable with existing GIS systems.
02Core Modelling Techniques for Imagery Analysis
2 sessions · 8 points
Session 1Classification, Segmentation and Object Detection
- Training convolutional neural network or vision transformer models for pixel-level land cover classification.
- Applying semantic segmentation to delineate features such as water bodies, buildings or crop fields.
- Detecting discrete objects such as vessels, vehicles or structures within large satellite scenes.
- Validating model predictions against field-surveyed ground truth points before trusting any output.
Session 2Change Detection and Time-Series Analysis
- Comparing multi-date imagery to flag land cover change such as deforestation or urban expansion.
- Calculating vegetation indices such as NDVI over time to track crop health and growth stage.
- Distinguishing genuine change from seasonal variation or sensor artefacts in the imagery.
- Building alerts that trigger automatically when change exceeds a defined threshold in a monitored area.
03Applications in Monitoring and Planning
2 sessions · 8 points
Session 1Environmental and Agricultural Monitoring
- Mapping deforestation and illegal land-use change for enforcement and compliance reporting.
- Estimating crop yield and irrigation need from vegetation indices combined with weather data.
- Estimating carbon stock changes for climate reporting and independent verification.
- Tracking informal settlement growth over time to inform urban service planning.
Session 2Infrastructure Planning and Disaster Response
- Supporting site selection and environmental impact assessment with land cover and terrain analysis.
- Mapping flood extent rapidly from post-event imagery to direct emergency response resources.
- Assessing structural damage after a disaster by comparing pre-event and post-event imagery.
- Prioritising affected areas for field assessment based on an automated damage score.
04Validation, Governance and Operational Deployment
2 sessions · 8 points
Session 1Ground-Truthing and Accuracy Assessment
- Designing a field survey sample that properly validates classification accuracy across every land cover class.
- Calculating producer's and user's accuracy from a confusion matrix built against ground truth data.
- Documenting model uncertainty for outputs used in regulatory contexts such as carbon credit verification.
- Retraining models when land cover patterns drift from those seen in the original training period.
Session 2Data Governance and Operational Scaling
- Addressing data sovereignty requirements when satellite coverage falls over another nation's territory.
- Managing the privacy implications of high-resolution imagery captured over populated areas.
- Automating ingestion and processing pipelines to keep monitoring dashboards current as new imagery arrives.
- Setting update frequency and alert thresholds appropriate to each monitoring use case's operational tempo.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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