Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery

2025-03-19

AI Summary

This study addresses the critical gap in globally consistent, spatiotemporally resolved data on renewable energy infrastructure. We construct the first open, global-scale, quarterly-updated database of commercial photovoltaic and onshore wind facilities, covering Q4 2017–Q2 2024. Leveraging >13 trillion pixels of high-resolution satellite imagery, we integrate deep learning–based semantic segmentation, facility-level geometric modeling, and multi-task joint inference (capacity inversion + land-use classification) to precisely identify 375,000 individual turbines and 86,000 power plants, while reconstructing their commissioning timelines and pre-construction land-cover changes. At the national level, remote-sensing–derived installed capacities align strongly with IRENA statistics (PV: R² = 0.96; onshore wind: R² = 0.93), substantially outperforming existing approaches. The fully open dataset provides a traceable, high-resolution spatiotemporal benchmark for energy transition assessment, policy analysis, and sustainability research.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

We present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines, derived from high-resolution satellite imagery analyzed quarterly from the fourth quarter of 2017 to the second quarter of 2024. We create this dataset by training deep learning-based segmentation models to identify these renewable energy installations from satellite imagery, then deploy them on over 13 trillion pixels covering the world. For each detected feature, we estimate the construction date and the preceding land use type. This dataset offers crucial insights into progress toward sustainable development goals and serves as a valuable resource for policymakers, researchers, and stakeholders aiming to assess and promote effective strategies for renewable energy deployment. Our final spatial dataset includes 375,197 individual wind turbines and 86,410 solar PV installations. We aggregate our predictions to the country level -- estimating total power capacity based on construction date, solar PV area, and number of windmills -- and find an $r^2$ value of $0.96$ and $0.93$ for solar PV and onshore wind respectively compared to IRENA's most recent 2023 country-level capacity estimates.

Problem

• Create global dataset of solar and wind energy installations.
• Estimate construction dates and land use types for installations.
• Assess renewable energy progress and support policy decisions.

Innovation

• Deep learning models analyze satellite imagery.
• Dataset covers 13 trillion pixels globally.
• Estimates construction dates and land use types.