Recently, 3D Gaussian Splatting has been introduced as a compelling alternative to NeRF for Earth observation, offering competitive reconstruction quality with significantly reduced training times. In this work, we extend the Earth Observation Gaussian Splatting (EOGS) framework to propose EOGS++, a novel method tailored for satellite imagery that directly operates on raw high-resolution panchromatic data without requiring external preprocessing. Furthermore, leveraging optical flow techniques we embed bundle adjustment directly within the training process, avoiding reliance on external optimization tools while improving camera pose estimation. We also introduce several improvements to the original implementation, including early stopping and TSDF post-processing, all contributing to sharper reconstructions and better geometric accuracy. Experiments on the IARPA 2016 and DFC2019 datasets demonstrate that EOGS++ achieves state-of-the-art performance in terms of reconstruction quality and efficiency, outperforming the original EOGS method and other NeRF-based methods while maintaining the computational advantages of Gaussian Splatting. Our model demonstrates an improvement from 1.33 to 1.19 mean MAE errors on buildings compared to the original EOGS models

翻译：近年来，三维高斯泼溅技术凭借其显著缩短的训练时间以及媲美NeRF的重建质量，已成为对地观测领域引人瞩目的替代方案。本研究在原有对地观测高斯泼溅框架基础上进行扩展，提出EOGS++方法——一种专为卫星影像设计的新颖方法，可直接处理原始高分辨率全色数据而无需外部预处理。同时，我们利用光流技术将束调整直接嵌入训练过程，既避免依赖外部优化工具，又提升了相机位姿估计精度。此外，我们对原始实现进行了多项改进，包括早停策略与TSDF后处理，这些改进共同促成了更清晰的重建结果与更优的几何精度。在IARPA 2016与DFC2019数据集上的实验表明，EOGS++在重建质量与效率方面均达到最先进水平，不仅优于原始EOGS方法及其他基于NeRF的方法，同时保持高斯泼溅的计算优势。相较于原始EOGS模型，我们的方法在建筑物平均绝对误差指标上实现了从1.33到1.19的改善。