US 12,333,786 B2
Time series generator trained using satellite data
Yueqi Li, San Jose, CA (US)
Assigned to DEERE & COMPANY, Moline, IL (US)
Filed by Deere & Company, Moline, IL (US)
Filed on Oct. 19, 2022, as Appl. No. 17/969,444.
Prior Publication US 2024/0135683 A1, Apr. 25, 2024
Prior Publication US 2024/0233337 A9, Jul. 11, 2024
Int. Cl. G06V 20/10 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 20/13 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/774 (2022.01) [G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 20/13 (2022.01); G06V 20/188 (2022.01); G06V 20/70 (2022.01); G06V 2201/10 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method implemented using one or more processors, the method comprising:
receiving an image having a first resolution, wherein the image having the first resolution depicts one or more agricultural conditions in a given region; and
processing the image having the first resolution using a trained generative model to generate a synthetic satellite image time series having a second resolution as an output of the trained generative model, the first resolution greater than the second resolution, and wherein the synthetic low resolution satellite image time series having the second resolution represent the one or more agricultural conditions, wherein the trained generative model is trained using a plurality of training examples, and wherein the plurality of training examples includes a first training example including (i) a respective satellite image having a third resolution capturing a respective region at a particular time and (ii) a corresponding satellite image time series having a fourth resolution capturing the respective region, the third resolution greater than the fourth resolution.