SyMTRS: Benchmark Multi-Task Synthetic Dataset for Depth, Domain Adaptation and Super-Resolution in Aerial Imagery
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 adapt...
| Authors | Safouane El Ghazouali, Nicola Venturi, Michael Rueegsegger et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
Models and datasets referencing this paper
- safouaneelg/SyMTRSDataset
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