Infinite Data Technology

Press Release

Zurich, 18th November 2023

In a significant leap forward for deep learning applications, TOELT has addressed a critical challenge in satellite imagery analysis: the lack of specific data for scenarios such as military and emergency response. Traditionally, the absence of data for training models in tasks like tracking vehicles or analyzing destruction has been a major hurdle. Additionally, disparate satellite technologies yield vastly different image formats, complicating the deployment of deep learning models in real-world scenarios.

TOELT's researchers have achieved a breakthrough by developing a technology capable of generating infinite synthetic data for any conceivable satellite imagery analysis scenario. This innovation combines various technologies with an advanced deep learning infrastructure, enabling the creation of diverse datasets. These datasets are not only instrumental for training and fine-tuning deep learning models but also for validating computer vision models.

An synthetically generated image of the Zurich airport with a few airplanes added artificially to the image. The planes have been added via 3D models of specific types of airplanes.

This development marks just the beginning of TOELT's vision. The synthetic data generated can be utilized for testing and validating tools and processes used in satellite imagery analysis. This includes applications in localization, segmentation, and semantic segmentation across various use cases, even under challenging conditions.

TOELT's technology allows for the addition of any vehicle or building into realistic satellite images, simulating countless scenarios for military planning, emergency simulations, destruction and fire detection, and vehicle tracking. The technology is versatile, accommodating the development of 3D models where none exist and simulating any satellite image resolution in various formats and sizes.

This breakthrough paves the way for generating an inexhaustible set of images, crucial for refining deep learning models through techniques like transfer learning and for testing existing tools and machine learning pipelines in any conceivable scenario.