AI and Open-data Driven Scalable Solar Power Profiling

TL;DR

This paper introduces an open, scalable AI-based framework for detecting rooftop solar panels from satellite imagery and generating detailed city-level solar power profiles, enhancing transparency and accessibility.

Contribution

It presents a novel approach using foundation vision AI models to detect solar panels from open data, avoiding manual labeling and proprietary tools, with an API for user-defined area analysis.

Findings

Achieved robust solar panel detection across heterogeneous satellite images.

Created georeferenced solar panel inventories for cities.

Developed an API enabling on-demand solar power profiling at specified locations.

Abstract

Solar photovoltaic (PV) deployment is expanding rapidly, yet detailed, up-to-date information on the spatial distribution and capacity of rooftop PV remains limited. This paper presents an open, scalable framework for detecting solar panels from open data and generating city-level solar power profiles. We leverage foundation vision AI models to detect solar panel geometries from open-source satellite imagery. This avoids manual data labeling and case-specific model training while maintaining robustness across heterogeneous imagery. Detected solar panels are converted into georeferenced polygons, yielding spatially explicit and incrementally extensible inventories. By integrating open weather data, we translate panel footprints into regional solar power profiles. The framework reduces dependency on proprietary imagery, manual labeling, and closed-source models, and offers a transparent and scalable approach for solar planning and analysis. We released the data and an API resulted from this work. For any user-specified building location, our API retrieves aerial imagery, detects rooftop solar panels, and returns georeferenced polygons. This empowers researchers and developers to scan user-defined areas to build solar panel maps and associated solar production profiles, thus facilitating advanced analysis like distributed solar production integration, local power flow optimization, energy tariff design, and infrastructure planning.