  One major barrier to advancing aerial autonomy has been collecting
large-scale aerial datasets for training machine learning models. Due to costly
and time-consuming real-world data collection through deploying drones, there
has been an increasing shift towards using synthetic data for training models
in drone applications. However, to increase widespread generalization and
transferring models to real-world, increasing the diversity of simulation
environments to train a model over all the varieties and augmenting the
training data, has been proved to be essential. Current synthetic aerial data
generation tools either lack data augmentation or rely heavily on manual
workload or real samples for configuring and generating diverse realistic
simulation scenes for data collection. These dependencies limit scalability of
the data generation workflow. Accordingly, there is a major challenge in
balancing generalizability and scalability in synthetic data generation. To
address these gaps, we introduce a scalable Aerial Synthetic Data Augmentation
(ASDA) framework tailored to aerial autonomy applications. ASDA extends a
central data collection engine with two scriptable pipelines that automatically
perform scene and data augmentations to generate diverse aerial datasets for
different training tasks. ASDA improves data generation workflow efficiency by
providing a unified prompt-based interface over integrated pipelines for
flexible control. The procedural generative approach of our data augmentation
is performant and adaptable to different simulation environments, training
tasks and data collection needs. We demonstrate the effectiveness of our method
in automatically generating diverse datasets and show its potential for
downstream performance optimization.

Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics;Machine Learning;Robotics