Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- super-resolution
+- monocular
+- depth
+- domain-adaptation
+pretty_name: SyMTRS
+size_categories:
+- 1K<n<10K
+---
+
+# SyMTRS: Synthetic Multi-Task Remote Sensing Dataset
+
+**SyMTRS** is a synthetic aerial imagery dataset generated using Unreal Engine 5 (UE5) to support **multi-task learning** in computer vision and generative AI research. The dataset is designed to benchmark and advance models across **depth estimation**, **domain adaptation**, and **super-resolution** — all within a consistent, high-quality simulated environment.
+
+It also provides a valuable resource for **generative AI applied to aerial and remote sensing imagery**.
+
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/63b3f7afb7fec0adf64eb8c0/O3rIEB0kdGFr5QGEI04Pi.png)
+
+---
+
+## 🌍 Overview
+
+SyMTRS (Synthetic Multi-Task Remote Sensing) provides photorealistic aerial scenes rendered in UE5, enabling precise ground truth generation that is difficult or expensive to obtain in real-world aerial data.
+
+The dataset is especially useful for:
+
+- Multi-task learning research  
+- Synthetic-to-real domain transfer  
+- Training data for generative aerial imagery models  
+- Benchmarking perception models under controlled environmental changes  
+
+---
+
+## 🧠 Supported Tasks
+
+### Domain Adaptation (Day → Night)
+Each aerial scene includes paired **daytime** and **nighttime** renders, enabling research on visual domain shifts.
+
+**Use cases**
+- Robust perception under illumination changes  
+- Unsupervised domain adaptation  
+- Nighttime aerial monitoring systems  
+
+---
+
+### Super-Resolution
+The dataset includes multiple resolution scales to support single-image super-resolution:
+
+| Scale | Description |
+|------|-------------|
+| ×2   | Moderate upscaling |
+| ×4   | High upscaling |
+| ×8   | Extreme upscaling |
+
+**Use cases**
+- Enhancing low-resolution satellite or drone imagery  
+- Improving detail recovery in aerial scenes  
+- Training diffusion or GAN-based upscalers  
+
+---
+
+## Why Synthetic Aerial Data?
+
+Real aerial datasets often lack:
+- Accurate depth ground truth  
+- Perfectly aligned day/night pairs  
+- Multi-scale image consistency  
+
+SyMTRS solves these issues through simulation, providing:
+- Pixel-perfect labels  
+- Controlled environmental variation  
+- Scalable data generation  
+
+---
+
+## Relevance to Generative AI
+
+SyMTRS is not only a perception dataset — it is also well-suited for **generative modeling** in aerial imagery:
+
+- Training diffusion or GAN models for aerial scene synthesis  
+- Learning structured scene representations  
+- Data augmentation for remote sensing  
+- Style and illumination transfer between domains  
+
+---
+
+### **(SOON)** Monocular Depth Estimation
+High-quality depth maps are rendered directly from the UE5 simulation engine, providing accurate ground truth for training and evaluation.
+
+**Use cases**
+- 3D scene understanding  
+- Terrain reconstruction  
+- Urban structure modeling  
+- Navigation and mapping  
+
+---
+
+## 📂 Dataset Structure
+
+The dataset is organized by **scene**, **task**, and **resolution level**. A typical structure may look like:
+
+```
+
+SyMTRS/
+│
+├── hr/                  # ORIGINAL RAW IMAGES
+│   ├── RS.0.png
+│   └── ...
+│
+├── night/               # NIGHT VERSION OF IMAGES
+│   ├── RS.0.png
+│   └── ...
+├── depth/               # DEPTH IMAGES
+│   ├── RS.depth.0.npy
+│   └── ...
+├── lr/                  # BICUBIC DOWNSAMPLED IMAGES
+│   ├── x2/
+│   │   ├── RS.0.png
+│   │   └── ...
+│   ├── x4/
+│   │   ├── RS.0.png
+│   │   └── ...
+│   ├── x8/
+│   │   ├── RS.0.png
+└   └   └── ...
+```
+
+
+---
+
+## 🔬 Potential Research Directions
+
+- Joint depth estimation + super-resolution models  
+- Domain-robust aerial perception systems  
+- Multi-task transformers for remote sensing  
+- Synthetic-to-real transfer learning  
+- Generative aerial world models  
+
+---
+
+## 📊 Dataset Size
+
+(SOON)
+
+---
+
+## 📜 License
+
+This dataset is released under the **Apache 2.0 License**, allowing both academic and commercial use with proper attribution.
+
+---
+
+## 🤝 Citation
+
+A PAPER WILL BE RELEASED.
+
+---
+
+## 🔗 Dataset Link
+
+**Hugging Face:**  
+https://huggingface.co/datasets/safouaneelg/SyMTRS
+
+---
+