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The GEOINT Playbook Summary

Core Content

The GEOINT Playbook outlines an investment thesis on the future of geospatial intelligence (GEOINT), focusing on the technological layers that make up the geospatial stack: Infrastructure, Distribution, and Applications. It highlights the evolution of geospatial data collection and analysis, emphasizing how advancements in technology have made geospatial intelligence more accessible, scalable, and impactful across various industries.

Main Views

1. Market Growth and Trends

• The global geospatial market is projected to grow from $63.1 billion to $147.6 billion over the next five years.
• The rise of "As-a-service" models has enabled scalable development and innovation in geospatial technologies.
• The industry is experiencing market consolidation, with traditional players and startups merging to create bundled solutions.
• Cloud computing, AI/ML, and APIs/SDKs are expanding the user base and applications of geospatial intelligence beyond data engineers and GIS specialists.

2. Historical Context

• NASA began developing remote sensing technology in the 1960s, leading to the launch of Landsat 1 in 1972.
• Esri was the first company to digitize mapping information for commercial use in the 1970s.
• The Landsat program has generated significant economic value, with estimates reaching $3.5 billion annually by 2017.
• The Landsat and Sentinel programs laid the foundation for modern geospatial platforms and data analysis.

3. Satellite Evolution

• There has been a 42% increase in remote sensing satellites from 2018 to 2021, with the global EO market expected to reach $7.9 billion by 2030.
• Satellites have become more cost-effective due to the commoditization of launch services and advancements in computing.
• SpaceX has drastically reduced launch costs, with the Falcon 9 costing $62 million and $2,500 per kg to LEO.
• GPUs and ASICs have transformed data processing capabilities, enabling real-time analysis of large geospatial datasets.
• Companies like Skybox Imaging and Planet have pioneered the use of small satellites and rapid iterative engineering to provide high-quality data at lower costs.

4. Geospatial Platforms

Satellites

• Pros:
  • Global monitoring, best for macro view
  • Large existing body of open source scientific imagery-based data products
  • High resolution data (10cm and beyond)
• Cons:
  • High CapEx relative to alternative methods
  • Expensive for resolutions < 2m
  • Challenging to leverage data without geospatial expertise

High Altitude Platforms (HAPs)

• Pros:
  • Monitor specific assets, best for persistent micro view
  • Cheaper data collection than satellites
  • Faster development and deployment timelines
  • High resolution data (10cm)
• Cons:
  • Specific regions/geographies (not global)
  • Experimental compared to traditional forms of collection

Aircraft

• Pros:
  • Monitors specific assets, strong for micro view
  • Cheaper data collection than satellites
  • Faster time to data collection
• Cons:
  • Specific regions/geographies (not global)
  • Expensive when compared to drones and HAPs

Drones & UAVs

• Pros:
  • Monitors specific assets, best for micro view
  • Cheaper data collection than satellites
• Cons:
  • Specific regions/geographies (not global)
  • Requires certified drone operators

Ground Sensors

• Pros:
  • Monitors specific assets, macro view at scale
  • Cheaper data collection vs satellites
  • Faster time to data collection vs satellites
  • Ability to monitor a specific place for long periods of time
  • Highest resolution data (1mm)
• Cons:
  • Specific regions/geographies but can reach scale with relatively inexpensive sensors deployed
  • Privacy concerns becoming more apparent

Key Information

• Geospatial data is now being captured at various altitudes using a range of technologies including satellites, HAPs, aircraft, drones, and ground sensors.
• Low-cost components, commoditized storage/compute, and decades of GIS development have made geospatial data more accessible.
• Cloud computing, AI/ML, and APIs/SDKs are enabling developers to build specialized applications without requiring deep geospatial expertise.
• SpaceX and NVIDIA have been pivotal in reducing costs and improving processing capabilities.
• The GEOINT stack is evolving from modular technology to vertically integrated software solutions, driven by the need for scalability and interoperability.
• Satellite data is becoming more valuable and accessible, with the potential to reshape industries and improve our understanding of the planet.

Conclusion

The GEOINT Playbook provides a comprehensive overview of the geospatial intelligence landscape, highlighting the convergence of technology, data, and applications. It underscores the importance of infrastructure in enabling the collection and analysis of geospatial data, and the distribution of this data through cloud and AI technologies. The application layer is where the true value of geospatial intelligence is realized, with new use cases emerging across agriculture, insurance, climate monitoring, and augmented reality. The future of geospatial intelligence is promising, with continued innovation expected to drive market growth and transform how we interact with the physical world.