AI and Open-data Driven Scalable Solar Power Profiling

2026-05-04

AI Summary

This study addresses the challenge of urban-scale solar energy planning, which is hindered by the lack of fine-grained, real-time data on the spatial distribution and capacity of rooftop photovoltaic (PV) systems. The authors propose a scalable, annotation-free framework that, for the first time, integrates foundation vision AI models with open-access satellite imagery to automatically detect rooftop PV panels and generate georeferenced polygons. By further incorporating open weather data, the method constructs regional power generation profiles. Designed for cross-regional robustness and transparency, the approach supports incremental updates. The project releases a city-scale PV geodatabase and an online API, enabling customizable area scanning, panel mapping, and solar yield estimation to support distributed energy analysis and grid planning.

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.

Problem

• solar photovoltaic
• rooftop PV
• spatial distribution
• solar power profiling
• open data

Innovation

• foundation vision models
• open-data
• solar PV detection
• georeferenced polygons
• scalable solar profiling