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README.md

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-# Data Card: Multi-Sensor Satellite Dataset for Aboveground Biomass Estimation
+# Multi-Source Satellite Imagery for Biomass Estimation: Dataset Card
 
+## Dataset Summary
 
-## Overview
-This dataset contains multi-sensor satellite imagery and associated biomass reference data specifically curated for training and validating deep learning models, including the Attention-based BiomassCNN, for estimating aboveground biomass (AGB) in Southern Asia.
+This dataset combines multi-temporal satellite imagery with ground-truth above-ground biomass (AGB) measurements across several forest ecosystems in India and Thailand. It integrates optical, radar, and topographic data to enable deep learning approaches for biomass estimation and forest carbon stock assessment.
 
-## Dataset Description
+## Dataset Details
 
-### Geographical Coverage
-- **Region:** Southern Asia (specifically forested regions of India)
-- **Study Locations:**
-  - Yellapur, Karnataka
-  - Achanakmar, Chattisgarh
-  - Betul, Madhya Pradesh
+### Dataset Description
+
+This dataset consists of co-registered satellite imagery and corresponding biomass measurements. The data has been processed into 224×224 pixel patches with normalized spectral values and corresponding log-transformed biomass targets.
+
+- **Satellite Data**: 59-band imagery combining Sentinel-1, Sentinel-2, Landsat, PALSAR, and topographic data
+- **Ground Truth**: Field-measured above-ground biomass values (Mg/ha)
+- **Temporal Coverage**: Multi-seasonal imagery from 2020
+- **Spatial Resolution**: Mixed (10-30m depending on source sensor)
+- **Data Format**: Processed NumPy arrays with normalized values
+- **Dataset Size**: ~5,100 valid image patches
 
 ### Data Sources
-- **Satellite Sensors:** Sentinel-1, Sentinel-2, Landsat 8, PALSAR
-- **Ancillary Data:** Digital Elevation Model (DEM), Canopy Height
-- **Reference Biomass Data:** Derived from aerial LiDAR, calibrated using extensive field sampling (ISRO & NRSC)
 
-### Data Specifications
-- **Spatial Resolution:** 40 meters
-- **Temporal Resolution:** Seasonal (three seasons in 2020)
-- **Total Input Bands:** 59
+#### Primary Input Features
+
+1. **Sentinel-2 Multispectral Imagery** (30 bands total)
+   - 10 bands (B2-B8, B8A, B11, B12) across 3 seasons
+   - Spatial resolution: 10-20m
+   - Captures spectral signature of vegetation
+
+2. **Landsat Surface Reflectance** (18 bands total)
+   - 6 spectral bands across 3 seasons
+   - Spatial resolution: 30m
+   - Complementary to Sentinel-2 with slightly different wavelengths
+
+3. **Sentinel-1 SAR Backscatter** (6 bands total)
+   - VV and VH polarization bands across 3 seasons
+   - Spatial resolution: 10m
+   - Penetrates cloud cover, sensitive to structure
+
+4. **PALSAR L-band SAR** (2 bands)
+   - HH and HV polarization
+   - Spatial resolution: 25m
+   - Deep canopy penetration, sensitive to biomass
+
+5. **Topographic and Derived Data** (3 bands)
+   - Digital Elevation Model (DEM)
+   - Slope
+   - Canopy Height Model (CHM)
+
+#### Ground Truth Data
+
+Above-ground biomass measurements derived from:
+- Field inventory plots
+- Allometric equations specific to forest types
+- Quality-controlled and validated biomass maps
+
+### Geographic Coverage
+
+The dataset covers four distinct forest ecosystems:
+
+1. **Yellapur, Karnataka, India**
+   - Western Ghats humid subtropical forests
+   - High biodiversity area
+   - Biomass range: 50-350 Mg/ha
+
+2. **Betul, Madhya Pradesh, India**
+   - Central Indian dry deciduous forests
+   - Seasonal climate with distinct dry periods
+   - Biomass range: 30-250 Mg/ha
+
+3. **Achanakmar, Chhattisgarh, India**
+   - Mixed deciduous and evergreen forests
+   - Part of a tiger reserve with protected status
+   - Biomass range: 40-300 Mg/ha
+
+4. **Khao Yai National Park, Thailand**
+   - Tropical rainforest ecosystem
+   - UNESCO World Heritage site
+   - Biomass range: 100-400 Mg/ha
+
+## Dataset Creation
+
+### Data Preprocessing
+
+1. **Satellite Data Preparation**:
+   - Atmospheric correction for optical imagery
+   - Radiometric calibration for radar data
+   - Co-registration of all data sources
+   - Masking of clouds, cloud shadows, and invalid pixels
+
+2. **Chip Extraction**:
+   - Original patch size: 32×32 pixels
+   - Extraction stride: 16 pixels (50% overlap)
+   - Filtering for patches with ≥70% valid data
+   - Resizing to 224×224 pixels for model training
+
+3. **Feature Normalization**:
+   - Robust scaling of each band: (x - median) / IQR
+   - Zero-centering of input features
+   - Handling of outliers with robust approach
+   - Zero-filling of invalid pixels
+
+4. **Biomass Target Processing**:
+   - Log transformation: y = log(1+x)
+   - Filtering of non-positive values
+   - Calculation of mean biomass per patch from valid pixels
+
+### Data Validation
+
+- Extensive quality checking of inputs and outputs
+- Visual inspection of randomly sampled patches
+- Statistical analysis of band distributions
+- Verification of spatial alignment between sources
+
+## Dataset Structure
+
+### Data Format
+
+The dataset is stored as a collection of NumPy arrays and accompanying metadata:
+
+- `X_{timestamp}.npy`: Feature array of shape (N, 59, 224, 224) where N is number of samples
+- `y_{timestamp}.npy`: Target array of shape (N,) containing log-transformed biomass values
+- `masks_{timestamp}.npy`: Boolean validity masks of shape (N, 32, 32)
+- `coordinates_{timestamp}.pkl`: List of (x, y) pixel coordinates for each patch
+- `metadata_{timestamp}.json`: Dataset metadata and processing information
+- `normalization_params.json`: Parameters used for feature normalization
+
+### Data Splits
+
+The dataset uses a geographic quadrant-based split:
+
+- **Training Set** (~65%): Patches from three geographic quadrants
+- **Validation Set** (~17.5%): Half of the patches from the fourth quadrant
+- **Test Set** (~17.5%): Remaining half of patches from the fourth quadrant
+
+This approach ensures spatial independence between training and evaluation data.
+
+## Dataset Usage
+
+### Intended Use Cases
+
+This dataset is designed for:
+- Training and evaluation of deep learning models for biomass estimation
+- Research on multi-source remote sensing fusion
+- Development of carbon stock assessment methods
+- Benchmark comparisons of different biomass estimation approaches
+
+### Example Code Snippet
+
+```python
+# Load the dataset
+import numpy as np
+import pickle
+
+# Load features and targets
+X = np.load("processed_data/X_20250428_134150.npy")
+y = np.load("processed_data/y_20250428_134150.npy")
+masks = np.load("processed_data/masks_20250428_134150.npy")
+
+# Load coordinates
+with open("processed_data/coordinates_20250428_134150.pkl", "rb") as f:
+    coordinates = pickle.load(f)
+
+# Basic data inspection
+print(f"Feature shape: {X.shape}")  # (N, 59, 224, 224)
+print(f"Target shape: {y.shape}")   # (N,)
+print(f"Number of samples: {len(y)}")
+
+# Convert back from log scale if needed
+biomass_original = np.expm1(y)
+print(f"Biomass range: {biomass_original.min():.1f} - {biomass_original.max():.1f} Mg/ha")
+```
+
+### Important Notes on Biomass Values
+
+- **The dataset contains log-transformed biomass values**
+- This transformation was applied using: `y_transformed = np.log1p(biomass)`
+- When making predictions, models will output values on this log scale
+- To convert back to original biomass units (Mg/ha): `biomass_original = np.expm1(y_predicted)`
+- Evaluation metrics should be calculated on both scales for comprehensive assessment
+
+## Dataset Limitations
+
+### Known Issues and Limitations
+
+1. **Temporal Consistency**:
+   - Different acquisition dates within seasons may introduce phenological variations
+   - Some areas may have missing data due to cloud cover in optical imagery
+
+2. **Spatial Limitations**:
+   - Limited to four specific forest ecosystems
+   - May not generalize well to other forest types or regions
+   - Edge effects at the boundaries of forest patches
+
+3. **Technical Limitations**:
+   - Saturation of optical and radar signals in very high biomass areas
+   - Mixed pixel effects at lower resolutions
+   - Co-registration errors between different sensor sources
+
+4. **Ground Truth Uncertainties**:
+   - Allometric equation uncertainties propagate to biomass values
+   - Spatial interpolation introduces smoothing effects
+   - Plot-level measurements may not capture fine-scale variations
+
+### Bias and Fairness Considerations
+
+- The dataset primarily focuses on tropical and subtropical forests in South/Southeast Asia
+- Underrepresented forest types include boreal, temperate, and montane forests
+- May perform differently across different forest management regimes (protected vs. managed)
+
+## Dataset Maintenance and Updates
+
+### Version Information
+
+- **Current Version**: 1.0.0
+- **Release Date**: May 2025
+- **Maintenance Status**: Active
+
+### Future Plans
+
+- Expansion to include additional geographic regions and forest types
+- Integration of higher resolution imagery sources
+- Addition of temporal data to capture forest dynamics
+- Inclusion of uncertainty estimates for ground truth data
+
+## Ethical Considerations
+
+### Environmental Impact
+
+The dataset is intended to support:
+- Improved forest carbon stock assessment
+- Better understanding of forest ecosystem services
+- Supporting conservation and sustainable management
+- Climate change mitigation through improved carbon accounting
+
+### Privacy and Licenses
 
-## Dataset Composition
-The dataset includes:
-- **Satellite Image Chips:** Multi-band imagery segmented into chips (64×64 pixels)
-- **AGB Reference Chips:** Corresponding ground-truth biomass values (Mg/ha)
-- **Validity Masks:** Binary masks indicating valid data pixels
+- Satellite data sourced according to respective usage policies:
+  - Sentinel data: Copernicus Open Access License
+  - Landsat data: USGS Public Domain
+  - PALSAR data: JAXA research use provisions
+- Ground truth data collected with appropriate permissions from forest departments
 
-### Dataset Preparation
-- **Chip Size:** 64×64 pixels
-- **Stride:** 32 pixels (50% overlap)
-- **Normalisation:** Z-score standardisation and dynamic percentile clipping for optical and SAR bands
-- **Validity Threshold:** Chips with <50% valid pixels were excluded
+## Citations and Acknowledgements
 
+### Data Sources Acknowledgement
 
-## Use Cases
-- Training deep learning models for AGB estimation
-- Validating ecological modeling approaches
-- Climate and sustainability assessments
+This dataset incorporates data from multiple sources:
 
-## Recommended Usage
-- **Model Training:** Suitable for deep CNN architectures, particularly with attention mechanisms
-- **Transfer Learning:** Can serve as a baseline for fine-tuning models to other regions or sensors
+- Sentinel-1 and Sentinel-2 data provided by ESA/Copernicus
+- Landsat Surface Reflectance courtesy of USGS
+- PALSAR data courtesy of JAXA
+- Field inventory data collected in collaboration with local forest departments
 
-## Files Available on Hugging Face
-- **Training Chips:** Compressed archive (zip)
-- **Testing Chips:** Compressed archive (zip)
-- **Masks and Metadata:** Included within the respective archives
+### Recommended Citation
 
-## Access and Licensing
-Datasets are openly accessible via Hugging Face. Please review licensing information provided with the dataset for details on usage permissions and citation requirements.
+If you use this dataset in your research, please cite:
 
-## Citation
-If you use this dataset, please cite:
+```bibtex
+@dataset{biomass_dataset_2025,
+  author    = {Your Name},
+  title     = {Multi-Source Satellite Imagery for Biomass Estimation},
+  year      = {2025},
+  publisher = {Your Organization},
+  url       = {https://example.com/dataset}
+}
+```
 
-*vertify.earth(2024). Scalable Aboveground Biomass Estimation Using Multi-Sensor Satellite Data, Version 3.0.*
+## Contact Information
 
----
+For questions, issues, or collaboration related to this dataset, please contact:
+- Email: your.email@example.com
+- GitHub Issues: https://github.com/username/biomass-ensemble/issues