Generating synthetic training data and training a remote sensing machine learning model

Abstract

Implementations are described herein for training a remote sensing machine learning model. In various implementations, ground-truth low elevation images that depict particular crop(s) in particular agricultural area(s) and terrain conditions observed in the agricultural area(s) are identified. A plurality of low elevation training images is generated based on the ground truth low elevation training images to include the plurality of ground truth low elevation images and synthetic low elevation images generated based on synthetic terrain conditions. The plurality of low-elevation training images are processed using a synthetic satellite image machine learning model to generate the plurality of synthetic satellite training images, which are then processed by the remote sensing machine learning model to generate inferred terrain conditions. The remote sensing machine learning model is then trained based on a comparison of the inferred terrain conditions and the corresponding observed or synthetic terrain conditions.