---
pretty_name: "Multimodal Monsoon Indian Dataset"
license: mit
language:
  - en
tags:
  - climate
  - monsoon
  - precipitation
  - earth-observation
  - satellite
  - era5
  - nowcasting
task_categories:
  - other
---


# Multimodal Monsoon Indian Dataset (1 km)

## 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.

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:

* 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:

```
@misc{mazumder2025learningregionalmonsoonpatterns,
      title={Learning Regional Monsoon Patterns with a Multimodal Attention U-Net}, 
      author={Swaib Ilias Mazumder and Manish Kumar and Aparajita Khan},
      year={2025},
      eprint={2509.23267},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.23267}, 
}
```

---

## License

This dataset is released under the **MIT License**. See `LICENSE` for details.