---
license: mit
tags:
- rooftop-solar
- solar-energy
---
# DeepSolar-3M

**🔨 Repo under active construction**

**📄 Paper:** [DeepSolar-3M: An AI-Enabled Solar PV Database Documenting 3 Million Systems Across the US](https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/iclr2025/55/paper.pdf)  
**📍 Conference:** [ICLR 2025 - Tackling Climate Change with Machine Learning Workshop](https://www.climatechange.ai/papers/iclr2025/55)

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## Overview

DeepSolar-3M provides fast, high-resolution mapping of rooftop photovoltaic (PV) systems across the United States.  
This repository contains county-level and blockgroup-level aggregated data from our AI pipeline.

**Key features:**
- Scalable detection of PV installations from aerial imagery 
- Blockgroup-level and county-level aggregation of PV system statistics
- Detailed breakdowns by system type (residential, commercial, utility-scale, solar heating)

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## 📊 County-Level Dataset

Each entry corresponds to a U.S. county (identified by FIPS code) and includes:

- **Total PV system count**  
- **Total PV area** (in square meters)  
- **Median PV area** (m²)  
- **Average PV area** (m²)

**Breakdown by system type (% of systems):**
- Residential systems
- Commercial systems
- Utility-scale systems
- Solar heating systems

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## 🗺️ Block Group-Level Dataset

Each entry corresponds to a U.S. Census block group (identified by GEOID/Block Group FIPS) and includes all the features listed above.

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## 📬 Citation

If you find this resource useful, please cite:

```
@inproceedings{prabha2025deepsolar3m,
  title={DeepSolar-3M: An AI-Enabled Solar PV Database Documenting 3 Million Systems Across the US},
  author={Prabha, Rajanie and Wang, Zhecheng and Zanocco, Chad and Flora, June and Rajagopal, Ram },
  booktitle={ICLR 2025 Workshop on Tackling Climate Change with Machine Learning},
  url={https://www.climatechange.ai/papers/iclr2025/55},
  year={2025}
}

```
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## Contact

Feel free to reach out in case you have any questions - ```rajanie@stanford.edu``` 

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