SyMTRS: Benchmark Multi-Task Synthetic Dataset for Depth, Domain Adaptation and Super-Resolution in Aerial Imagery

TL;DR

SyMTRS is a comprehensive synthetic aerial imagery dataset designed for multi-task learning, including depth estimation, domain adaptation, and super-resolution, facilitating controlled experiments with perfect ground truth.

Contribution

The paper introduces SyMTRS, a large-scale synthetic dataset that supports multiple remote sensing tasks simultaneously, filling gaps left by existing single-task datasets.

Findings

SyMTRS enables joint research in geometric understanding, domain robustness, and resolution enhancement.

The dataset provides high-resolution images, perfect depth maps, and multi-domain variants for comprehensive benchmarking.

Results demonstrate the dataset's utility in improving multi-task remote sensing models.

Abstract

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.