Remote sensing is crucial in various domains, such as agriculture, urban planning, environmental monitoring, and disaster management. However, acquiring real-world remote sensing data can be challenging due to cost, logistical constraints, and privacy concerns. To overcome these limitations, synthetic data has emerged as a promising approach. We present an overview of the use of synthetic data for remote sensing applications.In this regard, we address three conditions that can drastically affect the optimization of computer vision algorithms: lighting conditions, fidelity of the 3D model, and resolution of the synthetic imagery data. We propose a highly configurable pipeline called Synthetix as part of our GeoEngine platform for synthetic data generation. Synthetix allows us to quickly create large amounts of aerial and satellite imagery under varying conditions, given a few samples of 3D objects on real-world scenes. We demonstrate our pipeline’s effectiveness by generating 3D scenes from 35 real-world locations and utilizing these scenes to generate different versions of datasets and answer the three questions. We conduct an in-depth ablation study and show that considering different environments and weather conditions increases the reliability and robustness of the deep learning networks.