# Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery

## TL;DR

This paper explores using conditional GANs to generate synthetic remote sensing images and labels, enhancing data availability for object detection tasks and improving performance when training data is limited.

## Contribution

It introduces a novel GAN-based data augmentation method that generates both images and labels for remote sensing applications, aiding in domain adaptation.

## Key findings

- Synthetic data improves object detection accuracy in limited data scenarios.
- GAN-generated images and labels are plausible and useful for training.
- Data augmentation with GANs can mitigate data scarcity issues.

## Abstract

The difficulty in obtaining labeled data relevant to a given task is among the most common and well-known practical obstacles to applying deep learning techniques to new or even slightly modified domains. The data volumes required by the current generation of supervised learning algorithms typically far exceed what a human needs to learn and complete a given task. We investigate ways to expand a given labeled corpus of remote sensed imagery into a larger corpus using Generative Adversarial Networks (GANs). We then measure how these additional synthetic data affect supervised machine learning performance on an object detection task. Our data driven strategy is to train GANs to (1) generate synthetic segmentation masks and (2) generate plausible synthetic remote sensing imagery corresponding to these segmentation masks. Run sequentially, these GANs allow the generation of synthetic remote sensing imagery complete with segmentation labels. We apply this strategy to the data set from ISPRS' 2D Semantic Labeling Contest - Potsdam, with a follow on vehicle detection task. We find that in scenarios with limited training data, augmenting the available data with such synthetically generated data can improve detector performance.