## 2 Data and Methods
### Methodology  
In this study, we used over 700 Maxar satellite images to train, validate, and test our air quality estimation models. The imagery had a spatial resolution of 2.5 m, covering 4 cities, and came from the year 2010 (see details in Table 1). In the first stage, the model was trained and validated on combined data from three urban environments: London, Vancouver, and LA. This multi-location model was then tested to predict PM\({}_{2.5}\) and NO\({}_{2}\) concentrations in the same three locations, on a subset of 10% of the data unseen by the model. In the second stage, we validated the same trained model on NYC: an urban region completely unseen by the model. The same two-stage process was used to produce separate models for each air pollutant, and both models were trained and evaluated with LUR estimates as their target data (more details in the 'Data and Methods' section). Root mean squared error (RMSE), residuals (defined as the difference between the predicted values and the target data), and normalized RMSE (NRMSE, defined as RMSE divided by the difference between the 25 th and 75 th percentiles of the target PM\({}_{2.5}\)/NO\({}_{2}\) concentrations) were calculated to quantify model prediction errors. In addition, we compared our results to available ground monitoring stations' annual concentrations for PM\({}_{2.5}\) and NO\({}_{2}\). * Model Architecture  
We used a deep learning approach to develop a new model for estimating PM\({}_{2.5}\) and NO\({}_{2}\) concentrations. We modified a VGG16 model (developed by [38]), which is a deep convolutional neural network (CNN) originally tasked to classify objects in the ImageNet dataset. CNNs are a type of neural networks that are popular for their effective and efficient performances in dependent data structures such as time series and images [39]. CNNs consist of a stack of convolutional layers that will understand data patterns using internal data dependency. For instance, in images, each pixel is dependent on its neighboring pixels, and the image patterns are linked to not just a pixel but a collection of pixels in a neighborhood. Hence, CNNs are capable of learning spatial features, such as roads, buildings, trees, etc., in urban environments from satellite images. The aim of this study is to train a CNN that efficiently and effectively learns urban features and finds their relations to the PM\({}_{2.5}\) and NO\({}_{2}\) pollution level. To adapt the VGG16 model to our objective, we changed the original architecture from a classification model to a regression model by removing the final softmax activation function (resulting in a neuron with linear output). This enabled us to estimate continuous PM\({}_{2.5}\) and NO\({}_{2}\) concentration levels. We also removed some of the convolutional layers to account for the difference in size of our input imagery. The new model is trained to learn urban reflectance patterns and estimate their corresponding PM\({}_{2.5}\) and NO\({}_{2}\) concentrations (Figures 1 and S1). This approach was feasible due to the computational resources of the NASA Ames Research Center High-End Computing Capability GPU nodes. The proposed model is set up to take Maxar images with a spatial resolution of 2.5 m as input and output AQ maps with 100-200 m scale. The model will take a high-spatial-resolution image (meter-scale) as input and output a corresponding value with 100-200 m scale. Thus, we will not see the local pixelated behavior, and the model output will have the same resolution/detail level as the LUR target data. target data point. We randomly selected one of these patches for each point so that our dataset has only one patch corresponding to each target data point. Our imagery dataset has a disproportionate number of image patches for some cities over others, with LA having over three times as many patches as London. To prevent the model from favoring the image distribution of one city, we balanced the dataset by oversampling patches proportionally from the under-represented cities.