Recent advances in deep learning for remote sensing rely heavily on large annotated datasets, yet acquiring high-quality ground truth for geometric, radiometric, and multi-domain tasks remains costly and often infeasible. In particular, the lack of accurate depth annotations, controlled illumination variations, and multi-scale paired imagery limits progress in monocular depth estimation, domain adaptation, and super-resolution for aerial scenes. We present SyMTRS, a large-scale synthetic dataset generated using a high-fidelity urban simulation pipeline. The dataset provides high-resolution RGB aerial imagery (2048 x 2048), pixel-perfect depth maps, night-time counterparts for domain adaptation, and aligned low-resolution variants for super-resolution at x2, x4, and x8 scales. Unlike existing remote sensing datasets that focus on a single task or modality, SyMTRS is designed as a unified multi-task benchmark enabling joint research in geometric understanding, cross-domain robustness, and resolution enhancement. We describe the dataset generation process, its statistical properties, and its positioning relative to existing benchmarks. SyMTRS aims to bridge critical gaps in remote sensing research by enabling controlled experiments with perfect geometric ground truth and consistent multi-domain supervision. The results obtained in this work can be reproduced from this Github repository: https://github.com/safouaneelg/SyMTRS.

翻译：近年来，深度学习在遥感领域的进展高度依赖于大规模标注数据集，然而获取几何、辐射及多任务场景的高质量真值数据成本高昂且往往不可行。特别地，精确深度标注、可控光照变化及多尺度配对影像的缺失，制约了航空场景中单目深度估计、域自适应和超分辨率技术的发展。我们提出了SyMTRS——一个基于高保真城市仿真流程生成的大规模合成数据集。该数据集提供高分辨率RGB航空影像（2048×2048）、像素级精确深度图、面向域自适应的夜间对应影像，以及用于超分辨率的x2、x4和x8倍对齐低分辨率变体。与现有聚焦单一任务或模态的遥感数据集不同，SyMTRS被设计为统一的多任务基准，能够支持几何理解、跨域鲁棒性及分辨率增强的联合研究。我们描述了数据集的生成流程、统计特性及其与现有基准的对比定位。SyMTRS通过提供完美几何真值与一致多域监督下的可控实验，旨在弥合遥感研究中的关键缺口。本工作结果可通过以下GitHub仓库复现：https://github.com/safouaneelg/SyMTRS。