# Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

## TL;DR

This paper introduces a procedural method for generating synthetic remote sensing images using game engines, enhancing change detection performance when real data is scarce.

## Contribution

A novel pipeline for creating realistic synthetic remote sensing datasets to improve neural network change detection under limited data conditions.

## Key findings

- Synthetic datasets improve model performance.
- Pipeline accelerates convergence of neural networks.
- Method is efficient and scalable.

## Abstract

Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant. However, in some instances, such as change detection in remote sensing images, annotated data cannot be obtained in sufficient quantities. In this work, we propose a simple and efficient method for creating realistic targeted synthetic datasets in the remote sensing domain, leveraging the opportunities offered by game development engines. We provide a description of the pipeline for procedural geometry generation and rendering as well as an evaluation of the efficiency of produced datasets in a change detection scenario. Our evaluations demonstrate that our pipeline helps to improve the performance and convergence of deep learning models when the amount of real-world data is severely limited.