Space Technology │ Artificial Intelligence

Private Crowds Create the Largest Satellite Imagery Set on the Planet

Trained data specialists at Wovenware help create and maintain high- quality datasets for satellite imagery.

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“Maxar Technologies Inc.’s integrated space infrastructure and Earth intelligence capabilities that make global change visible, information actionable and space accessible for their customers in more than 70 countries across the globe.”

Identifying opportunities together

AI algorithms usually get all the attention, but in reality, data is what’s most important. Accurate AI models rely on well identified, classified and labeled data. Wovenware has played a key role in building Maxar’s image annotation toolsets and labeling some of its most ambitious satellite image datasets.

Setting a goal for success

Our seasoned team of developers work with our trained data specialists to create a mass production line of satellite data.

Our challenges and learnings

  • Correctly and effectively identify, classify and label objects in millions of satellite images.
  • Develop custom toolsets to facilitate management, oversight and review of the image annotation process.
  • Provide reliable tracking of images and objects tagged and reviewed for each image.

The Wovenware Approach

Accurately labeling millions of high-resolution images requires trained annotators with specialized toolsets. Wovenware worked with a team led by Maxar Technologies to enhance its proprietary Deep Workbench tool with more advanced annotation, project management and quality features.

Our annotators, all based in Puerto Rico, underwent extensive on-boarding to fully understand Maxar’s specific business needs, so that they could consistently identify and label objects in satellite images with extreme accuracy and precision. This resulted in training datasets of the highest quality. Now, the Deep Workbench platform eliminates the administrative overhead of managing projects and provides easy to use annotation tools, streamlining the process and significantly cutting the time it takes to label and review images.

In addition to creating the dataset and custom annotating platform, our team of data scientists built computer vision models to automatically detect and classify objects based on the xView dataset.

Technologies we take advantage of