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How The Era of GeoAI Platformization Unlocks Insights from Space

By Nihar Ranjan Sahoo, Senior Principal Architect, Geospatial Technology, Cyient

Today, the space above us is dominated by over a thousand operational satellites, most of them capturing vast data streams—ranging from aerial imagery to thermal infrared. Our potential for earth observation has been truly unlocked. The convergence of Earth Observation (EO) technology and Artificial Intelligence (AI) is reshaping how industries harness this deluge of information, paving the way for sustainable practices and transformative insights.

Cloud titans like Amazon, Google, and Microsoft have recognized the potential in managing vast data sets from satellite constellations. They are not just storing and processing; they are forging direct connections between data centers and satellite broadband ground stations through edge computing, reducing latency and ensuring rapid analysis.

Two major players, AWS Ground Station and Azure Orbital, have empowered satellite operators, consolidating spacecraft management into a unified console. This move streamlines control, data retrieval, storage, and the application of cutting-edge AI/ML processing.

The trajectory of Earth Observation indicates robust growth, with a projected CAGR of 8.3%, reaching $25.273 billion by 2040, according to Morgan Stanley. Furthermore, GeoAI is slated to experience a staggering 12.2% CAGR, soaring from $67.4 billion in 2022 to $119.9 billion by 2027, as forecasted by M&M.

This landscape is shaped by four key pillars:

1. Proliferating EO Data: Over a thousand Earth Observation satellites continually capture a myriad of data, signaling a data-rich era.

2. Progressive Hardware Companies: Hardware manufacturers and satellite operators are shifting from image capture to offering Value-Added Services (VAS).

3. Hyperscalars: Industry giants like Google, Amazon, and Azure provide vast computing capabilities and invest in satellite technology, redefining the earth observation landscape.

4. GeoAI Providers: Emerging players such as Orbital Insight, Descartes Labs, and Blackshark AI are making significant contributions to the evolving GeoAI domain.

This smorgasboard of data becomes invaluable when harnessed for knowledge extraction. Creating a platform that automates the pipeline—from data capture to real-time inference—is vital for supporting diverse use cases, including asset detection, environmental analysis, and change monitoring.

Hyperscalers like Azure, AWS, and Google, with their end-to-end services, provide the computational power necessary for processing vast volumes of information. The platformization of space data is revolutionizing insights, empowering businesses globally. Hyperscalers are crafting exclusive platforms for Earth Observation, integrating data capture, pre-processing, geospatial analytics, and complementing them with allied services. This shift has given rise to innovative use cases in agriculture, sustainability, and real-time monitoring.

Platform-as-a-Service (PaaS) providers like AWS and Azure not only facilitate data access but also orchestrate end-to-end pipelines—from capture to analytics. Geospatial MLOps has emerged as a game-changer, enabling reliable and efficient deployment and maintenance of machine learning models for Earth Observation imagery in production.

Leading the charge, Amazon Sagemaker Geospatial and AzureML, along with their services, offer end-to-end ML pipeline execution. Google Earth Engine combines a vast catalog of satellite imagery with planetary-scale analysis capabilities.

Specialized players like Airbus, Maxar, BlackSky, and PlanetLabs, alongside solution providers such as Picterra, Alteryx, and Neptune, are extracting locational intelligence from diverse data sets. Cyient, with decades of industry experience, focuses on verticalized specialization, market-agnostic APIfication, and a universal processing pipeline.

There are numerous factors heralding the proliferation of Earth Observation data. As the data grows robust, AI and machine learning are able to train their models and deliver results with greater accuracy and efficacy. In all, within the next decade, our potential to understand space is set to grow exponentially. 

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