Microsoft Technology Licensing, LLC

Peder Andreas Olsen

Ranveer Chandra

Krishna Chintalapudi

Behnaz Arzani

Tsu-wang Hsieh

Shadi Abdollahian Noghabi

Olaoluwa Adigun

Chenning Li

Mingmin Zhao

Shadi Noghabi

A computing device including a processor configured to receive satellite status data from satellites included in a satellite constellation. The processor is further configured to determine a link topology of the satellites. Based at least in part on the satellite status data and the link topology, the processor is further configured to identify a first satellite constellation subset including one or more selected satellite pairs. Identifying the one or more selected satellite pairs includes computing respective link utility values associated with a plurality of candidate pairs of satellites included in the satellite constellation based at least in part on the satellite status data and the link topology. The one or more selected satellite pairs are selected based at least in part on the link utility values. The processor is further configured to transmit, to the satellites included in the first satellite constellation subset, instructions to perform intersatellite imaging data transfer.

Intersatellite imaging data transfer

The description relates to context-aware management of satellite images. One example can track a satellite relative to locations on earth, ground stations, and other satellites and identify parameters associated with the tracked satellite. The example can manage images captured by the satellite for individual locations based upon the identified parameters.

Satellite image management

A computing device is provided, including a processor configured to receive imaging relevance data for a geographic area. The processor may be further configured to generate, based at least in part on the imaging relevance data, image mask instructions specifying a region of interest included in the geographic area. The processor may be further configured to transmit the image mask instructions to a satellite. The processor may be further configured to receive, from the satellite, filtered satellite image data of the region of interest. One or more deprioritized regions of the geographic area outside the region of interest may be excluded from the filtered satellite image data.

Mask for satellite image data

Systems and methods for generating predicted high-resolution images from low-resolution images. To generate the predicted high-resolution images, the present technology may utilize machine learning models and super resolution models in a series of processes. For instance, the low-resolution images may undergo a sensor transformation based on processing by a machine learning model. The low-resolution images may also be combined with land structure features and/or prior high-resolution images to form an augmented input that is processed by a super resolution model to generate an initial predicted high-resolution image. The predicted initial high-resolution image may be combined or stacked with other predicted high-resolution images to form a stacked image. That stacked image may then be processed by another super resolution model to generate a final predicted high-resolution image.

Super resolution for satellite images

Examples disclosed herein are related to using a machine learning model to generate image data. One example provides a system, comprising one or more processors, and storage comprising instructions executable by the one or more processors to obtain image data comprising an image with unoccluded features, apply a mask to the unoccluded features in the image to form partial observation training data comprising a masked region that obscures at least a portion of the unoccluded features, and train a machine learning model comprising a generator and a discriminator at least in part by generating image data for the masked region and comparing the image data generated for the masked region to the image with unoccluded features.

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Redmond, WA, United States of America

Peder Andreas Olsen