Abstract: Generating one or more high-resolution atmospheric gas concentration maps using geography-informed machine learning includes obtaining a remote sensing dataset constrained by at least one temporal window and at least one spatial window defining a first geographic area. The remote sensing dataset includes at least a first set of atmospheric gas concentration data for a plurality of atmospheric gases. A training dataset is generated based on the remote sensing dataset. A machine learning model is trained with the training dataset to predict a plurality of atmospheric gas concentration values for at least one atmospheric gas of the plurality of atmospheric gases in a given geographic area and with a spatial resolution that is greater than a spatial resolution of atmospheric gas concentration data provided as an input to the machine learning module.

Abstract: A geography-informed machine learning (GIML) model is trained on a first remote sensing dataset corresponding to a first geographic area and including a first set of atmospheric gas concentration data for at least one atmospheric gas, a first set of multispectral data, and a first set of spatially autocorrelated land use classifications. The GIML model receives input including a second remote sensing dataset corresponding to a second geographic area. The second remote sensing dataset includes a second set of atmospheric gas concentration data for the atmospheric gas, a second set of multispectral data, and a second set of spatially autocorrelated land use classifications. The GIML model generates, for the second geographic area, a plurality of predicted atmospheric gas concentration values for the atmospheric gas having a spatial resolution that is greater than a spatial resolution of the first and second sets of atmospheric gas concentration data.

Abstract: A silhouette image and a style image are input to a convolutional neural network to produce respective content feature layer and pattern feature layers. A reference image is input into the convolutional neural network to determine reference feature layers. For each of a plurality of iterations, a combination loss of the convolutional neural network is minimized to obtain an output image comprising an abstraction of the style image within confines of the silhouette image. The combination loss includes a content loss based on the content feature layers and a style loss based on the pattern feature layers.

Abstract: A silhouette image and a style image are input to a convolutional neural network to produce respective content feature layer and pattern feature layers. A reference image is input into the convolutional neural network to determine reference feature layers. For each of a plurality of iterations, a combination loss of the convolutional neural network is minimized to obtain an output image comprising an abstraction of the style image within confines of the silhouette image. The combination loss includes a content loss based on the content feature layers and a style loss based on the pattern feature layers.