## 1 Introduction  
Semantic segmentation using neural networks has enabled rapid advances in machine vision and scene understanding in recent years [1]. Acquiring adequate labeled training data is often the most significant challenge when using machine learning. Recent work has shown that using physics-based simulation can increase the amount and diversity of labeled training data while simultaneously reducing the cost and time required to collect and semantically label raw data [2]. The problem with current approaches that use simulation is that it is difficult to automatically create digital assets--synthetic terrains with desired characteristics--without a human in-the-loop. In this work, we implement a technique where the digital assets are created automatically and regenerated for different images, thereby introducing both automation and randomization into the training process. To address and reduce the substantial labeled data requirements of modern machine learning techniques, active learning has become an area of intense research. Active learning in its traditional formulation allows a machine learning algorithm to query an oracle to label unsupervised data during training [3]. Current research in active learning is directed towards selecting the most useful sample to label automatically. However, recent work has reversed the selection problem: instead of selecting the best sample to label from a pre-generated set, the problem is instead that of selecting a sample to generate. This can be in the form of moving a drone for novel viewing perspectives [4] or even selecting simulation parameters used to generate synthetic data batches [5]. More recent work has shown that automated machine-learning can be achieved without human intervention in the model training phase [6]. However, these results were achieved by using publicly available datasets of cats and dogs (CIFAR-10) and everyday objects (ImageNet). As these labeled datasets still required a tremendous of time and manpower to acquire and label, the process is not truly automated. In contrast, this work describes a fully automated pipeline for both generation, labeling, and training machine learning. Object detection in aerial images is a challenging task as some objects are only a few pixels wide, some objects are occluded, and some are in the shade. With the cost of drones decreasing, there is a surge in the amount of aerial data, so it will be useful if models can extract valuable features from the aerial data. Convolutional neural networks (CNN) are a very useful tool for machine learning applications [7]. The CNN architecture combines the benefits obtained by a standard neural network training with the convolution operation to efficiently classify images. Further, being a neural network, the CNN reduces the images into a form that is easier to process and scalable for large datasets without losing features critical towards a good prediction. Pooling layers reduces the spatial size by extracting a representative value which reduces computational cost. Fully connected layers produce the final classification with features from the previous layers resulting in the output. Figure 1 shows the schematic diagram of a basic CNN architecture. The R-CNN (Region Based Convolutional Neural Network) and faster R-CNN frameworks deal with the problem of efficient object localization in object detection [7]. These are based on two-stage detection where the first stage generates a sparse set of candidate object locations, and the second stage classifies each candidate location as one of the foreground and background classes using a CNN. As the two-stage detectors are slower, recent work focuses on one-stage detectors such as You Only Look Once (YOLO) [8] and single-shot multibox detector (SSD) [9]. The RetinaNet [10] one-stage object detection models have demonstrated promising results over existing single stage detectors.