Built to ScaleEarth Observant's engineering designs, software architectures, and business processes leverage off-the-shelf components, micro services, and IT infrastructure-as-a-service to keep costs down.
Our commercial data strategy is "cloud-first" and based on open standards from the Open Geospatial Consortium and Open Source Geospatial Foundation, and enabled via AWS Ground Station and C2S. Earth Observant will deliver imagery in machine-readable formats for automated analysis while still maintaining backward compatibility with legacy systems and human interactions. Emerging standards we follow include: Cloud Optimized GeoTIFF (COG), Spatio-Temporal Asset Catalogs (STAC), and Analysis Ready Data (ARD). |
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Phase 1 - Satellites as a Service
Earth Observant will serve customers who can benefit from access to a small satellite constellation to support crucial decision making. This includes leasing time/capacity over preferred geographic locations to national governments and their partners for defense and high-value commercial applications. This business approach has been proven successful time and again over the last two decades. SaaS will be an important ongoing revenue stream for Earth Observant.
Markets: Defense & Intelligence, National Governments, and Large Commercial Customers
Markets: Defense & Intelligence, National Governments, and Large Commercial Customers
Ground Architecture Schema
Phase 2 - Data as a Service
As EOI’s constellation and revisit rates grow, so does our ability to service more markets. Increased frequency and coverage enables better monitoring in areas of interest for a wide range of industries. Traditional users of earth imagery are very familiar with using the web for search and previewing but are often frustrated by slow fulfillment. EOI is focused on minimizing the path from our satellites to the cloud and to getting timely data into customers' hands fast.
Many organizations are investing in their own cloud-based solutions where automation, scalability, and costs can be dynamically scaled up or down. EOI's "cloud-first" data products will be organized into different online feeds hosted with marketplace providers like Amazon Web Services, Google Cloud, and Microsoft Azure.
Many organizations are investing in their own cloud-based solutions where automation, scalability, and costs can be dynamically scaled up or down. EOI's "cloud-first" data products will be organized into different online feeds hosted with marketplace providers like Amazon Web Services, Google Cloud, and Microsoft Azure.
Markets:
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Energy & Resource Management
Environmental & Disaster Assessment |
Asset Monitoring & Tracking
Transportation & Logistics Planning |
Infrastructure Mapping
Public Safety & Homeland Security |
Insurance & Real Estate
Cadastral & Property Appraisal |
Phase 3 - Analytics as a Service
The rapid development of machine learning (ML) based analytics is having a big impact in many industries. This trend will continue as organizations reap new benefits. EOI has partnered with the leaders in artificial intelligence (AI) to feed their data-hungry algorithms with our future ultra high-resolution earth imagery on a global scale.
Our cloud partners have built into their platforms support for AI & ML. EOI will leverage these substantial investments to make our earth imagery available to a wide spectrum of government and commercial users. EOI's business model is based on providing a continuously updated stream of content. Our analytics customers pay only for the pixels they use, which is a major advantage of maintaining algorithms in the cloud.
The below "deep learning" workflow is possible entirely in the cloud.
Our cloud partners have built into their platforms support for AI & ML. EOI will leverage these substantial investments to make our earth imagery available to a wide spectrum of government and commercial users. EOI's business model is based on providing a continuously updated stream of content. Our analytics customers pay only for the pixels they use, which is a major advantage of maintaining algorithms in the cloud.
The below "deep learning" workflow is possible entirely in the cloud.
1) Artificial Intelligence (AI) - convolutional neural network (CNN).
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2) Earth imagery assisted feature recognition & extraction results.
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3) Typical analysis reports.
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