Restructure README to focus on project vision and clarify available features vs future plans

Co-authored-by: Ritesh313 <36135489+Ritesh313@users.noreply.github.com>

Author: Copilot

README.md

@@ -1,165 +1,115 @@
-# NEON Multi-Modal Tree Species Classification Dataset
+# NEON Multi-Modal Tree Species Classification
 
-A comprehensive dataset of 167 tree species with 47,971 individual tree crowns from 30 NEON sites across North America. Each sample includes RGB imagery, 369-band hyperspectral data, and LiDAR canopy height models.
+A comprehensive toolkit for multi-modal tree species classification using NEON ecological data. This project combines RGB imagery, hyperspectral data, and LiDAR to enable accurate tree species identification across diverse North American ecosystems.
 
-## Dataset Overview
+## Project Vision
 
-**Dataset Statistics:**
-- 47,971 individual tree crowns
-- 167 unique species
-- 30 NEON sites across North America
-- 10 years of data (2014-2023)
-- 3 modalities: RGB (3 bands), Hyperspectral (369 bands), LiDAR CHM (1 band)
-- Ecological metadata: Height (95.4% available), stem diameter (99.4% available), canopy position (81.4% available)
+This repository aims to provide an end-to-end solution for tree species classification:
 
-**Dataset Configurations:**
+- **Dataset**: Ready-to-use multi-modal tree crown dataset with 167 species (currently available)
+- **Data Processing**: Tools for downloading and processing raw NEON data products (in development)
+- **Classification Models**: Pre-trained models and training pipelines (in development)
+- **DeepForest Integration**: Automated crown detection and classification workflow (planned)
 
-The dataset comes with 3 pre-configured subsets for different use cases:
+## What's Available Now
 
-| Configuration | Samples | Species | Description |
-|---------------|---------|---------|-------------|
-| `combined` | 47,971 | 167 | Complete dataset with all available samples |
-| `large` | ~42,000 | ~162 | Main training set |
-| `high_quality` | ~5,500 | ~96 | Curated subset with highest data quality |
+### Multi-Modal Dataset
 
-**Data Format:**
-- HDF5 storage for efficient compressed format and fast loading
-- CSV metadata files with crown IDs, species labels, site information, and ecological measurements
+A curated dataset of 47,971 individual tree crowns from 30 NEON sites, ready for immediate use:
 
-## Installation
+- **167 tree species** from diverse North American ecosystems
+- **3 modalities**: RGB (3 bands), Hyperspectral (369 bands), LiDAR CHM (1 band)
+- **10 years of data** (2014-2023) with ecological metadata
+- **3 configurations**: `combined` (47,971 samples), `large` (~42,000 samples), `high_quality` (~5,500 samples)
+- **HDF5 format**: Efficient storage with automatic download (590 MB)
 
-**Requirements:**
-- Python 3.9+ (recommended: Python 3.11)
-- CUDA-capable GPU (optional, recommended for training)
+## Quick Start
 
-**Installation Steps:**
+### Installation
 
 ```bash
-# Clone the repository
 git clone https://github.com/Ritesh313/NeonTreeClassification.git
 cd NeonTreeClassification
-
-# Install with uv (recommended)
-uv sync
-
-# Or install with pip
-pip install -e .
+uv sync  # or: pip install -e .
 ```
 
-## Getting Started
-
-### Quick Start Example
-
-Run the quickstart script to verify installation and download the dataset:
-
-```bash
-# Using uv
-uv run python quickstart.py
-
-# Or after activating environment
-source .venv/bin/activate
-python quickstart.py
-```
-
-The dataset (590 MB) will automatically download on first use.
-
-### Using Dataloaders
-
-**Basic Usage:**
+### Get the Dataset
 
 ```python
 from scripts.get_dataloaders import get_dataloaders
 
-# Get dataloaders (dataset downloads automatically on first use)
+# Dataset downloads automatically (590 MB)
 train_loader, test_loader = get_dataloaders(
     config='large',
     modalities=['rgb', 'hsi', 'lidar'],
     batch_size=32
 )
 
-# Each batch contains:
+# Use in your training loop
 for batch in train_loader:
-    rgb_data = batch['rgb']        # torch.Tensor [batch_size, 3, 128, 128]
-    hsi_data = batch['hsi']        # torch.Tensor [batch_size, 369, 12, 12]
-    lidar_data = batch['lidar']    # torch.Tensor [batch_size, 1, 12, 12]
-    labels = batch['species_idx']  # torch.Tensor [batch_size]
+    rgb = batch['rgb']          # [batch_size, 3, 128, 128]
+    hsi = batch['hsi']          # [batch_size, 369, 12, 12]
+    lidar = batch['lidar']      # [batch_size, 1, 12, 12]
+    labels = batch['species_idx']  # [batch_size]
 ```
 
-**Training Scenarios:**
+Or run the quickstart example:
+```bash
+uv run python quickstart.py
+```
 
-```python
-# Standard training on large dataset
-train_loader, test_loader = get_dataloaders(config='large', test_ratio=0.2)
+## Coming Soon
 
-# Maximum data training
-train_loader, test_loader = get_dataloaders(config='combined', test_ratio=0.15)
+**Data Processing Pipeline**: Tools for processing raw NEON data products are being finalized and will be released for public use. This will enable users to:
+- Download NEON tiles for all three modalities
+- Crop individual tree crowns from shapefiles
+- Create custom datasets with their own crown annotations
 
-# High-quality subset only
-train_loader, test_loader = get_dataloaders(config='high_quality', test_ratio=0.2)
+**Classification Models**: Pre-trained models and training scripts for tree species classification will be added to the repository.
 
-# Domain transfer (train on large, test on high_quality)
-train_loader, test_loader = get_dataloaders(
-    train_config='large',
-    test_config='high_quality'
-)
-```
+**DeepForest Integration**: Planned integration with [DeepForest](https://github.com/weecology/DeepForest) to enable:
+- Automatic crown detection from aerial imagery
+- Seamless multi-modal data extraction for detected crowns
+- Direct classification using pre-trained models from this repository
 
 ## Dataset Details
 
-**Top Species:**
-
-| Rank | Species | Count | Percentage |
-|------|---------|-------|------------|
-| 1 | Acer rubrum L. | 5,684 | 11.8% |
-| 2 | Tsuga canadensis (L.) Carrière | 3,303 | 6.9% |
-| 3 | Pseudotsuga menziesii (Mirb.) Franco var. menziesii | 2,978 | 6.2% |
-| 4 | Pinus palustris Mill. | 2,207 | 4.6% |
-| 5 | Quercus rubra L. | 2,086 | 4.3% |
+**Top 5 Species:**
+1. Acer rubrum L. (5,684 samples, 11.8%)
+2. Tsuga canadensis (L.) Carrière (3,303 samples, 6.9%)
+3. Pseudotsuga menziesii (Mirb.) Franco var. menziesii (2,978 samples, 6.2%)
+4. Pinus palustris Mill. (2,207 samples, 4.6%)
+5. Quercus rubra L. (2,086 samples, 4.3%)
 
-**Geographic Distribution:**
-
-Data collected from 30 NEON sites across North America. Top 5 sites:
+**Top 5 Sites:**
 - HARV: 7,162 samples (14.9%)
 - MLBS: 5,424 samples (11.3%)
 - GRSM: 4,822 samples (10.1%)
 - DELA: 4,539 samples (9.5%)
 - RMNP: 3,931 samples (8.2%)
 
 **NEON Data Products:**
-- RGB: DP3.30010.001 - High-resolution orthorectified imagery
-- Hyperspectral: DP3.30006.002 - 426-band spectrometer reflectance
-- LiDAR: DP3.30015.001 - Canopy Height Model
-
-**Data Structure:**
-
-The metadata CSV contains:
-- `crown_id`: Unique identifier for each tree crown
-- `species`: Species code
-- `species_name`: Full species name
-- `site`: NEON site code
-- `year`: Data collection year
-- `height`: Tree height in meters
-- `stemDiameter`: Stem diameter in cm
-- `canopyPosition`: Light exposure level
-- `rgb_path`, `hsi_path`, `lidar_path`: Paths to data in HDF5 file
+- RGB: DP3.30010.001 (High-resolution orthorectified imagery)
+- Hyperspectral: DP3.30006.002 (426-band spectrometer reflectance)
+- LiDAR: DP3.30015.001 (Canopy Height Model)
 
-## Documentation
+For complete dataset documentation, training guides, and advanced usage, see the [docs/](docs/) directory.
 
-For detailed documentation, see the `docs/` directory:
-- [Advanced Usage](docs/advanced_usage.md) - Custom filtering, Lightning DataModule, and advanced features
-- [Training Guide](docs/training.md) - Model training examples and baseline results
-- [Visualization Guide](docs/visualization.md) - Data visualization tools and examples
-- [Processing Pipeline](docs/processing.md) - NEON data processing workflow
+## Citation
 
-## Contributing
+If you use this dataset in your research, please cite:
 
-1. Fork the repository
-2. Create a feature branch
-3. Submit a pull request
-
-See [CONTRIBUTING.md](CONTRIBUTING.md) for more details.
+```bibtex
+@dataset{neon_tree_classification_2024,
+  title={NEON Multi-Modal Tree Species Classification Dataset},
+  author={[Author Names]},
+  year={2024},
+  publisher={GitHub},
+  url={https://github.com/Ritesh313/NeonTreeClassification}
+}
+```
 
 ## Acknowledgments
 
-- National Ecological Observatory Network (NEON)
-- Dataset statistics generated on 2025-08-28
+National Ecological Observatory Network (NEON)
+