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Data-driven methods such as convolutional neural networks (CNNs) are known todeliver state-of-the-art performance on image recognition tasks when thetraining data are abundant. However, in some instances, such as changedetection in remote sensing images, annotated data cannot be obtained insufficient quantities. In this work, we propose a simple and efficient methodfor creating realistic targeted synthetic datasets in the remote sensingdomain, leveraging the opportunities offered by game development engines. Weprovide a description of the pipeline for procedural geometry generation andrendering as well as an evaluation of the efficiency of produced datasets in achange detection scenario. Our evaluations demonstrate that our pipeline helpsto improve the performance and convergence of deep learning models when theamount of real-world data is severely limited.

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