Planet Makes Its Geospatial Data Available Through Amazon SageMaker
Planet Labs PBC (NYSE: PL) has announced a collaboration with Amazon Web Services to integrate geospatial data within Amazon SageMaker, enhancing machine learning capabilities for their customers. This partnership allows users to access daily satellite imagery, facilitating quicker model training and visualization. With the integration, Planet's satellite data can streamline processes previously deemed complex and inaccessible. The new tools significantly reduce training times, enabling operational efficiencies for users in various industries.
- Collaboration with Amazon Web Services enhances ML capabilities for clients.
- Integration allows easy access to daily satellite imagery for data analysis.
- Accelerated model training and deployment reduces time from months to minutes.
- None.
Planet operates the largest constellation of earth observation satellites in the world, with the capacity to provide daily medium- and high-resolution imagery of Earth’s landmass every day. Planet is using AWS to better serve its customers who can now benefit from the simplicity and speed of Amazon SageMaker’s new geospatial ML capabilities to build, train, and deploy ML models using Planet’s geospatial data at up to 10x the speed. These enhanced capabilities create new opportunities for Planet customers to accelerate data access within geospatial tools and cloud platforms.
Due to the challenging work required to use geospatial data for ML, access to ML has historically been out of reach for many geospatial data customers. With Amazon SageMaker, customers can pull in their proprietary data sources, such as Planet satellite data, from Amazon Location Service and AWS Data Exchange. It’s a first of its kind partnership and the only on-demand, high cadence satellite imagery ML model training, inference, and visualization platform available in the market.
“Planet understands the challenges associated with ingesting and supplying mass volumes of data,” said Planet President
“Building, training, and deploying ML models using geospatial data is a manual process for most organizations. The primary barrier is the lack of purpose-built capabilities for performing ML on these types of massive datasets,” said
Purposely built for geospatial data, Amazon SageMaker geospatial ML capabilities offer easy to use tools to orchestrate data chunking and pre-processing operations for satellite imagery, creating web-based visualizations, and creating seamless scaling up for large datasets. The pre-built algorithms can reduce development to just a few days and one-click deployment to the cloud, freeing up customers' time.
Planet customers are invited to bring Planet data into the new Amazon SageMaker geospatial ML capabilities to provide early-stage feedback, beginning in
About
Planet is a leading provider of global, daily satellite imagery and geospatial solutions. Planet is driven by a mission to image the world every day, and make change visible, accessible and actionable. Founded in 2010 by three NASA scientists, Planet designs, builds, and operates the largest Earth observation fleet of imaging satellites, capturing over 30 TB of data per day. Planet provides mission-critical data, advanced insights, and software solutions to over 800 customers, comprising the world’s leading agriculture, forestry, intelligence, education and finance companies and government agencies, enabling users to simply and effectively derive unique value from satellite imagery. Planet is a public benefit corporation trading on the
Planet Labs PBC Forward-Looking Statements
Except for the historical information contained herein, the matters set forth in this press release are forward-looking statements within the meaning of the "safe harbor" provisions of the Private Securities Litigation Reform Act of 1995, including, but not limited to, Planet Labs PBC’s ability to capture market opportunity and realize any of the potential benefits from current or future product enhancements, new products, or strategic partnerships and customer collaborations. Forward-looking statements are based on Planet Labs PBC’s management’s beliefs, as well as assumptions made by, and information currently available to them. Because such statements are based on expectations as to future events and results and are not statements of fact, actual results may differ materially from those projected. Factors which may cause actual results to differ materially from current expectations include, but are not limited to the risk factors and other disclosures about
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Source: Planet
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