Over the last decade, a large number of optical, radar, and laser remote-sensing satellites were launched. Today, they provide daily global observation of the surface and enable extracting insights from the data that can be used in various industries from agriculture monitoring to mining and urban development. There are certain challenges to using satellite data, including low latency and accessibility issues caused by the pattern of mission operation and spacecraft orbit; large frame size (while in practice the area of interest may cover only 1% of the scene); and interoperability of the datasets from different sensors of different providers. All these challenges can be addressed based on using public cloud processing infrastructure with serverless processing nodes.
Drawing on a practical example of a project to build a satellite-processing infrastructure—which covers the whole set of tasks from on-orbit operations to downlink, cross-calibration, and imaging product delivery—Alex Kudriashova walks you through designing and building an entire processing infrastructure and discusses its challenges.
Alex Kudriashova leads data product integration at Astro Digital, a micro-satellite company building custom space missions. She participated in emergency response to Forest Fires in Amazonia in 2019 and the consequent agriculture monitoring with multispectral satellite data.
Previously, she was a co-founder of ImageAiry, an online marketplace for satellite imaging services, and worked on B2B software solutions at Dell. Her interests are Open Source, Big Data, and Business Intelligence. She developed her Computer Science and Technical leadership skills while studying at MIT, course 6.
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