Module 1: Introduction to Geospatial Intelligence (GEOINT)
Concepts:
What is GEOINT? (Integration of GIS, remote sensing, and intelligence analysis).
Evolution of GEOINT – from military cartography to modern cloud-based systems.
GEOINT cycle: tasking, collection, processing, exploitation, dissemination.
Importance in decision-making for defense, security, and humanitarian operations.
Applications: Military reconnaissance, disaster relief, border monitoring, climate security.
Tools: ArcGIS Pro, QGIS for spatial analysis; Google Earth for visualization.
Practical Exercise: Explore and visualize intelligence datasets (satellite imagery + vector data) in GIS software.
Module 2: Data Sources and Acquisition for GEOINT
Concepts:
Types of data: raster (imagery, LiDAR), vector (roads, infrastructure), tabular (population, socio-economic).
Sources: commercial satellites (Maxar, Planet), government missions (Sentinel, Landsat), drones/UAVs, crowdsourced (OpenStreetMap, Ushahidi).
Open-source vs proprietary datasets.
Data accuracy, resolution, and reliability for intelligence use.
Applications: Surveillance mapping, humanitarian logistics, crisis data acquisition.
Tools: Sentinel Hub, Copernicus Open Access Hub, ArcGIS Online, QGIS plugins.
Practical Exercise: Download Sentinel-2 imagery for a conflict-affected area and preprocess it for analysis.
Module 3: Remote Sensing in GEOINT Applications
Concepts:
Basics of remote sensing: spectral signatures, multispectral vs hyperspectral data.
Identifying terrain, infrastructure, and vegetation.
Change detection (before/after imagery).
Radar data (SAR) for detecting structures, ships, or hidden features.
Applications: Monitoring deforestation, detecting illegal mining, infrastructure analysis.
Tools: ENVI, ArcGIS Image Analyst, Google Earth Engine.
Practical Exercise: Perform land cover classification on Sentinel imagery to detect expansion of urban areas.
Module 4: Spatial Analysis Techniques for Intelligence
Concepts:
Core spatial analysis methods: buffering, overlays, proximity analysis.
Terrain modeling: slope, elevation, viewshed, line-of-sight.
Hotspot and density analysis for event clustering (crime, insurgency, disaster).
Applications: Selecting military base locations, mapping safe evacuation routes.
Tools: ArcGIS Spatial Analyst, QGIS Processing Toolbox.
Practical Exercise: Conduct a suitability analysis for an emergency relief camp considering elevation, roads, and flood zones.
Module 5: Cartographic Design for GEOINT
Concepts:
Intelligence-focused map design principles.
Symbology standards (NATO military symbols, humanitarian icons).
Importance of clarity, readability, and visual hierarchy.
Effective use of annotation, labeling, and color schemes.
Applications: Situational awareness maps, field operation briefings.
Tools: ArcGIS Pro Layouts, QGIS Print Composer.
Practical Exercise: Create a crisis operations map (roads, shelters, hazard zones) for a flood response briefing.
Module 6: Geospatial Intelligence in Defense and Security
Concepts:
GEOINT for surveillance and reconnaissance.
Border monitoring and smuggling/trafficking route mapping.
Maritime domain awareness (ship tracking).
Integration of GEOINT with other intelligence (SIGINT, HUMINT, OSINT).
Applications: Counter-terrorism, border security, illegal trade monitoring.
Tools: ArcGIS Pro with Tracking Analyst, QGIS Time Manager.
Practical Exercise: Analyze cross-border trafficking routes using vector and satellite data.
Module 7: GEOINT for Disaster Response and Humanitarian Assistance
Concepts:
Using GEOINT in risk assessment and crisis response.
Rapid mapping after disasters (earthquake, flood, wildfire).
Integration of real-time/crowdsourced data (Twitter, OpenStreetMap, drones).
Applications: Humanitarian logistics, evacuation planning, food and medical aid distribution.
Tools: ArcGIS Online Dashboards, QGIS Disaster Management Plugins, Ushahidi.
Practical Exercise: Build a flood risk map, identify safe evacuation routes, and create a response dashboard.
Module 8: Predictive Analytics and Modeling in GEOINT
Concepts:
Predictive modeling for risk anticipation (insurgency, migration, disaster impact).
Spatial statistics and machine learning (e.g., hotspot prediction).
Agent-based modeling and scenario simulations.
Applications: Forecasting refugee flows, predicting conflict-prone areas, anticipating wildfire spread.
Tools: ArcGIS Pro ModelBuilder, Python (scikit-learn, GeoPandas).
Practical Exercise: Use historical data to predict likely areas of future insurgent activity.
Module 9: Data Integration, Security, and Ethics in GEOINT
Concepts:
Data fusion: integrating imagery, maps, databases, and real-time feeds.
Security protocols for handling sensitive geospatial data.
Ethical considerations: privacy, surveillance vs humanitarian use.
Applications: Multi-agency data sharing, national security, open-source vs classified data.
Tools: ArcGIS Enterprise, PostgreSQL/PostGIS, secure web GIS platforms.
Practical Exercise: Design a secure workflow for managing intelligence data with restricted access controls.
Module 10: Capstone Project and Case Studies in GEOINT
Concepts:
Bringing together GEOINT theory and practice.
Real-world case studies (Haiti earthquake 2010, Ukraine conflict, African drought crisis).
Project: Participants design and present a GEOINT product, such as:
A situational awareness dashboard for humanitarian relief.
A border monitoring system using remote sensing and vector analysis.
A predictive threat/risk map for urban security.
Presentation: Groups deliver a GEOINT intelligence briefing using maps, dashboards, and reports.
