  To model and forecast flight delays accurately, it is crucial to harness
various vehicle trajectory and contextual sensor data on airport tarmac areas.
These heterogeneous sensor data, if modelled correctly, can be used to generate
a situational awareness map. Existing techniques apply traditional supervised
learning methods onto historical data, contextual features and route
information among different airports to predict flight delay are inaccurate and
only predict arrival delay but not departure delay, which is essential to
airlines. In this paper, we propose a vision-based solution to achieve a high
forecasting accuracy, applicable to the airport. Our solution leverages a
snapshot of the airport situational awareness map, which contains various
trajectories of aircraft and contextual features such as weather and airline
schedules. We propose an end-to-end deep learning architecture, TrajCNN, which
captures both the spatial and temporal information from the situational
awareness map. Additionally, we reveal that the situational awareness map of
the airport has a vital impact on estimating flight departure delay. Our
proposed framework obtained a good result (around 18 minutes error) for
predicting flight departure delay at Los Angeles International Airport.
