Update README.md

README.md

@@ -67,27 +67,89 @@ configs:
     path: data/train-*
 license: apache-2.0
 pretty_name: PrediTree
+tags:
+- remote-sensing
+- multi-temporal
+- multi-spectral
+- canopy-height-prediction
+- 3-pg
+- infrared
+- rgb
+- model
 ---
 
-# Temporal Multispectral Canopy Dataset
+# 🌳 PrediTree: A Multi-Temporal Sub-Meter Canopy Dataset
 
-## Dataset Description
-This dataset provides multi-temporal, multi-spectral imagery and canopy height information for remote sensing, forest monitoring, and environmental analysis. All data are alligned at 0.5m resolution.
+[![Dataset](https://img.shields.io/badge/🤗-Dataset-blue.svg)](https://huggingface.co/datasets/hiyam-d/vhr_canopy_height_allier_50cm_small)
+[![Paper](https://img.shields.io/badge/📄-Paper-green.svg)](https://arxiv.org/)
+[![License](https://img.shields.io/badge/License-Apache--2.0-yellow.svg)](https://www.apache.org/licenses/LICENSE-2.0)
 
-### Features
-| Column             | Description |
-|-------------------|-------------|
-| `chm`             | Canopy Height Model (CHM) in meters. |
-| `rgb_1`, `rgb_2`, `rgb_3` | RGB imagery captured at three time periods (year 1, 2, 3). |
-| `irc_1`, `irc_2`, `irc_3` | Infrared imagery for three time periods (year 1, 2, 3). |
-| `chm_mean_year`    | Average canopy height across years. |
-| `rgb_irc_year_1`, `rgb_irc_year_2`, `rgb_irc_year_3` | Acquisition year of combined RGB + infrared imagery for three time periods (year 1, 2, 3). |
+![Sample Panels](./sample.png)
 
-## Citation
+## 📖 Overview
+**PrediTree** is a large-scale **multi-temporal, multi-spectral canopy height dataset** designed for 🌍 **remote sensing, forestry monitoring, and environmental analysis**.  
+All imagery and canopy height products are **spatially aligned** at **0.5 m resolution**, enabling fine-grained tree growth prediction and ecological studies.
+
+---
+
+## ✨ Key Highlights
+- 📊 **Multi-Temporal**: 3 yearly acquisitions (RGB + NIR + NDVI)  
+- 🌈 **Multi-Spectral**: High-resolution optical imagery including RGB, NIR, and derived NDVI  
+- 🌲 **Canopy Height Models (CHM)**: LiDAR-based ALS reference data  
+- 📏 **Resolution**: 0.5 m — among the highest available at continental scale  
+- 🌍 **Coverage**: France-wide dataset with departmental splits  
+- 📦 **Scale**: 785k training patches, ~880 GB of data  
+
+---
+
+## 📂 Dataset Structure
+Each sample contains:
+| Column | Description |
+|--------|-------------|
+| `chm` | 🌲 Canopy Height Model (m) |
+| `rgbnir_ndvi_[1-3]` | 📸 RGB + NIR + NDVI imagery for three years (5 bands, 256×256) |
+| `rgbnir_year_[1-3]` | 📅 Acquisition year for imagery |
+| `chm_mean_year` | 🏞️ Average canopy height across years |
+| `no_data_percentage` | ❌ % missing pixels |
+| `crs`, `transform`, `bounds`, `resolution` | 🗺️ Geospatial metadata |
+
+---
+
+## 📊 Dataset Specs
+```yaml
+splits:
+  train:
+    num_examples: 785,392
+    size: 880 GB
+resolution: 0.5 m
+download_size: 730 GB
+dataset_size: 880 GB
+license: apache-2.0
+```
+
+---
+
+## 🔬 Scientific Context
+PrediTree builds on prior canopy height mapping efforts. Compared to single-temporal or coarser-resolution datasets, it is the **first to offer multi-temporal sub-meter CHM-aligned imagery at national scale**.
+
+![Comparison with Existing Datasets](./comparison.png)
+
+---
+
+## 📜 Citation
+If you use this dataset, please cite:
+
+```bibtex
 @inproceedings{debary2025preditree,
   title={PrediTree: A Multi-Temporal Sub-meter Dataset of Multi-Spectral Imagery Aligned With Canopy Height Maps},
   author={Debary, Hiyam and Fiaz, Mustansar and Klein, Levente},
   booktitle={GAIA},
   year={2025},
   url={https://huggingface.co/datasets/hiyam-d/vhr_canopy_height_allier_50cm_small}
-}
+}
+```
+
+---
+
+## 🔖 Tags
+`remote-sensing` · `multi-temporal` · `multi-spectral` · `canopy-height-prediction` · `infrared` · `rgb` · `model`