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
📍 Conference: ICLR 2025 - Tackling Climate Change with Machine Learning Workshop
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)
📊 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
🗺️ 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.
📬 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}
}
Contact
Feel free to reach out in case you have any questions - rajanie@stanford.edu