Synthetic Remote Sensing Data with High-Accuracy Simulation Powered by RIT’s DIRSIG Available for Rendered.ai Customers Worldwide

Rendered.ai, the leading platform for physics-based synthetic data, and the Rochester Institute of Technology’s Digital Imaging and Remote Sensing (DIRS) Laboratory announced additional collaboration that enables expanded access to the DIRSIG physics-driven synthetic imagery model through Rendered.ai’s cloud-based platform for high-volume synthetic data generation.

With the increasing collection of remote sensing imagery around the world, Machine Learning (ML) algorithms using Computer Vision (CV) data will be essential for extracting knowledge and insight from Earth Observations data that has the potential to be used by governments and commercial customers. Rendered.ai provides a cloud-based platform as a service (PaaS) for data scientists and CV engineers to engineer and generate large, configurable synthetic CV datasets for training Artificial Intelligence (AI) and ML systems.

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“Rendered.ai is pleased to have established a unique working relationship with the RIT team in which we will be able to offer access to DIRSIG for synthetic data generation broadly across our customer base,” said Nathan Kundtz, Ph. D., Founder and CEO of Rendered.ai. “Working with the DIRS Lab team enables us to fulfill two objectives upon which our company was founded, overcoming the challenges experienced by scientists and CV engineers of using real sensor data and enabling customers to access high-quality synthetic content without requiring specialization in simulation technologies.”

The DIRSIG model is well known in the Earth Observation industry for producing a simulated output representing passive single-band, multi-spectral, and hyper-spectral imagery from the visible through the thermal infrared region of the electromagnetic spectrum. DIRSIG is widely used by imagery analysts and scientists to test algorithms and to train on simulated imagery content.

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“We are excited to expand our collaboration with Rendered.ai which enables us to reach users throughout the scientific and government community through the cloud,” said Scott Brown, Ph.D., principal scientist and project lead. “The demand for access to high-accuracy, physically correct synthetic imagery is increasing as more Earth Observation vehicles are launched and Rendered.ai is helping us to reach even more users with our deep investment in scientifically accurate simulation.”

Providing access to DIRSIG within Rendered.ai will open access to users across the globe who may previously have been limited by training or travel requirements from gaining access to the software. Rendered.ai helps customers by packaging DIRSIG and other imagery simulation capability in reusable channels that may be collaboratively configured and executed to generate large batches of synthetic data.

To learn more and to explore how DIRSIG can be packaged in Rendered.ai, sign up for a trial account and use the content code “DIRSIGDEMO” to get started with a workspace set up to show you what is possible with DIRSIG on the cloud. The trial also provides access to getting started information including videos and our how-to blog on synthetic data with DIRSIG.

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