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The number of daily sUAS operations in uncontrolled low altitude airspace isexpected to reach into the millions in a few years. Therefore, UAS densityprediction has become an emerging and challenging problem. In this paper, adeep learning-based UAS instantaneous density prediction model is presented.The model takes two types of data as input: 1) the historical density generatedfrom the historical data, and 2) the future sUAS mission information. Thearchitecture of our model contains four components: Historical DensityFormulation module, UAS Mission Translation module, Mission Feature Extractionmodule, and Density Map Projection module. The training and testing data aregenerated by a python based simulator which is inspired by the multi-agent airtraffic resource usage simulator (MATRUS) framework. The quality of predictionis measured by the correlation score and the Area Under the Receiver OperatingCharacteristics (AUROC) between the predicted value and simulated value. Theexperimental results demonstrate outstanding performance of the deeplearning-based UAS density predictor. Compared to the baseline models, forsimplified traffic scenario where no-fly zones and safe distance among sUASsare not considered, our model improves the prediction accuracy by more than15.2% and its correlation score reaches 0.947. In a more realistic scenario,where the no-fly zone avoidance and the safe distance among sUASs aremaintained using A* routing algorithm, our model can still achieve 0.823correlation score. Meanwhile, the AUROC can reach 0.951 for the hot spotprediction.

Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction