Airborne dust, including Dust storms and weaker dust traces, can have deleterious and hazardous effects on human health, agriculture, solar power generation, and aviation. Although earth observing satellites are extremely useful in monitoring dust using visible and infrared imagery, dust is often difficult to visually identify in single band imagery due to its similarities to clouds, smoke, and underlying surfaces. Furthermore, night-time dust detection is a particularly difficult problem, since radiative properties of dust mimic those of the cooling, underlying surface. The creation of false-color red-green-blue (RGB) composite imagery, specifically the EUMETSAT Dust RGB, was designed to enhance dust detection through the combination of single bands and band differences into a single composite image. However, dust is still often difficult to identify in night-time imagery even by experts. We developed a Deep Learning, UNET image segmentation model to identify airborne dust at night leveraging six GOES-16 infrared bands, with a focus on infrared and water vapor bands.The UNET model architecture is an encoder-decoder Convolutional Neural Network that does not require large amounts of training data, localizes and contextualized image data for precise segmentation, and provides fast training time for high accuracy pixel level prediction. This presentation highlights collection of the training database, development of the model, and preliminary model validation. With further model development, validation, and testing in a real-time context, probability-based dust prediction could alert weather forecasters, emergency managers, and citizens to the location and extent of impending dust storms.