Update README.md

README.md

@@ -2,40 +2,197 @@
 pretty_name: "Multimodal Monsoon Indian Dataset"
 license: mit
 language:
-- en
+  - en
 tags:
-- climate
-- monsoon
-- precipitation
-- earth-observation
-- satellite
-- era5
-- nowcasting
+  - climate
+  - monsoon
+  - precipitation
+  - earth-observation
+  - satellite
+  - era5
+  - nowcasting
 task_categories:
-- other
+  - other
 ---
-## Dataset Description
 
-**Multimodal Monsoon Indian Dataset** is a high-resolution (1 km) Earth observation dataset
-curated for monsoon precipitation classification and analysis across Indian states.
 
-The dataset integrates multiple modalities including:
-- Satellite-derived indices (e.g., NDVI, LST)
-- Reanalysis climate variables (ERA5)
-- Static geospatial context (elevation, land cover)
-- Pixel-wise precipitation labels
+# Multimodal Monsoon Indian Dataset (1 km)
 
-It is designed for research in:
-- Climate AI
-- Precipitation nowcasting
-- Multimodal learning
-- Spatial deep learning
+## Overview
 
+The **Multimodal Monsoon Indian Dataset** is a curated, high-resolution (≈1 km) Earth-observation dataset designed for **monsoon precipitation classification and analysis across Indian states**. The dataset integrates **satellite imagery, climate reanalysis variables, and static geospatial features** into a unified, state-wise structure suitable for multimodal deep learning.
 
-## Structure
-- data/: state-wise folders (GeoTIFFs/CSVs/masks as exported)
-- metadata/: variable sources + label definitions
-- scripts/: placeholder for data curation scripts (GEE exports etc.)
+This repository contains **raw exported artifacts** generated via reproducible Jupyter notebooks (one per state), preserving provenance and enabling flexible downstream preprocessing. The dataset is intended for research in:
 
-## Notes
-- This repo currently contains raw exported artifacts as-is (no renaming) to preserve provenance.
+* Climate AI
+* Precipitation classification and nowcasting
+* Multimodal & geospatial deep learning
+* High-resolution monsoon analysis over India
+
+---
+
+## Geographic Coverage
+
+The dataset currently includes state-level data for:
+
+* **Assam**
+* **Bihar**
+* **Himachal Pradesh**
+* **Karnataka**
+* **Kerala**
+
+Each state was processed independently using the **same notebook pipeline**, ensuring methodological consistency across regions.
+
+---
+
+## Spatial & Temporal Resolution
+
+* **Spatial resolution:** ~1 km × 1 km
+* **Spatial extent:** State administrative boundaries
+* **Temporal coverage:** Monsoon-season periods (state-specific, aligned with data availability)
+* **Projection:** As exported from source datasets (see `metadata/`)
+
+---
+
+## Modalities
+
+Each state folder contains multiple modalities aligned spatially and temporally.
+
+### 1) Satellite-Derived Features
+
+Examples include (depending on state and availability):
+
+* NDVI (vegetation index)
+* Land Surface Temperature (LST)
+* Spectral bands and derived indices
+
+**Source:** Satellite products accessed and exported via **Google Earth Engine (GEE)**.
+
+### 2) Climate & Reanalysis Variables
+
+* ERA5-derived atmospheric variables (e.g., temperature, humidity, wind, precipitation aggregates)
+* Gridded climate fields resampled/aligned to the target resolution
+
+**Source:** **ERA5 reanalysis**.
+
+### 3) Static Geospatial Context
+
+* Elevation / topography
+* Land-use / land-cover (LULC)
+* Other static spatial priors relevant to precipitation modeling
+
+### 4) Labels
+
+* Pixel-wise or region-wise precipitation labels (classification-oriented)
+* Label definitions, thresholds, and mappings are documented in `metadata/`
+
+---
+
+## Directory Structure
+
+```
+.
+├── data/
+│   ├── Assam/
+│   ├── Bihar/
+│   ├── Himachal_Pradesh/
+│   ├── Karnataka/
+│   └── Kerala/
+│       ├── *.tif        # GeoTIFF raster layers
+│       ├── *.csv        # Tabular exports (where applicable)
+│       └── masks/       # Label masks / precipitation classes
+│
+├── metadata/
+│   ├── variable_sources.yaml
+│   ├── label_definitions.yaml
+│   └── state_metadata.json
+│
+├── scripts/
+│   └── README.md        # Placeholder for GEE / preprocessing scripts
+│
+├── README.md
+└── LICENSE
+```
+
+> **Note:** File names and formats are preserved *as exported* to maintain provenance and traceability.
+
+---
+
+## Notebook-Based Data Generation
+
+Each state dataset was generated using a **dedicated Jupyter notebook** (e.g., `Assam.ipynb`) that follows the same high-level pipeline:
+
+1. Define the state boundary and spatial grid
+2. Query satellite and climate datasets via Google Earth Engine / reanalysis APIs
+3. Export aligned raster layers at ~1 km resolution
+4. Generate precipitation labels
+5. Save outputs as GeoTIFF / CSV artifacts
+
+The same notebook logic was reused (with state-specific parameters) for **Assam, Bihar, Himachal Pradesh, Karnataka, and Kerala**, ensuring **methodological consistency** across all regions.
+
+---
+
+## How to Use
+
+This dataset is provided as **raw geospatial files**, not as pre-extracted ML tensors.
+
+### Example (Python): Load a raster
+
+```python
+import rasterio
+
+with rasterio.open("data/Assam/ndvi.tif") as src:
+    ndvi = src.read(1)
+```
+
+Users are expected to:
+
+* Construct spatial patches or tiles
+* Perform normalization / temporal aggregation
+* Align modalities according to their experimental design
+
+This design choice keeps the dataset **model-agnostic** and suitable for diverse research pipelines.
+
+---
+
+## Dataset Viewer Note
+
+> **Note:** The Hugging Face dataset viewer is not applicable for this dataset since it consists of geospatial raster files rather than tabular samples. The displayed “rows” correspond to file-level metadata, not ML training instances.
+
+---
+
+## Intended Use & Limitations
+
+**Intended use:**
+
+* Research and benchmarking of multimodal precipitation models
+* Regional monsoon analysis
+* Spatial deep learning experiments
+
+**Limitations:**
+
+* Raw exported artifacts; users must implement task-specific preprocessing
+* Temporal coverage may vary slightly by state
+* Not intended for operational forecasting without further validation
+
+---
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{mazumder2026monsoon,
+  author       = {Mazumder, Swaib Ilias},
+  title        = {Multimodal Monsoon Indian Dataset},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/swaib/Multimodal_Monsoon_Indian_Dataset}
+}
+```
+
+---
+
+## License
+
+This dataset is released under the **MIT License**. See `LICENSE` for details.