It has become possible to use satellites to observe food growing at a global scale—using daily satellite images to glean agriculture-specific insights like the presence of farming activity, the presence of irrigation systems, crop classification, and productivity assessment. A pipeline starts with a set of images specifically designed for daily monitoring the growth of commodity crops: corn, soybean, rice, and wheat. This data is processed using a processing and delivery system with AI/ML (boosting) to understand vegetation patterns and AI for scaling the models on other climate zones.
Alex Kudriashova offers an overview of current publicly available satellite imagery data and explains how to inject it into your data pipeline and train and deploy AI/ML models based on it.
Aleksandra Kudriashova leads data product integration at Astro Digital, a platform for fast and easy access to satellite imagery. Previously, she was a cofounder 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 honed her computer science and technical leadership expertise at MIT.
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