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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.14860 |
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| _version_ | 1866916657141645312 |
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| author | Robinson, Caleb Ortiz, Anthony Kim, Allen Dodhia, Rahul Zolli, Andrew Nagaraju, Shivaprakash K Oakleaf, James Kiesecker, Joe Ferres, Juan M. Lavista |
| author_facet | Robinson, Caleb Ortiz, Anthony Kim, Allen Dodhia, Rahul Zolli, Andrew Nagaraju, Shivaprakash K Oakleaf, James Kiesecker, Joe Ferres, Juan M. Lavista |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14860 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery Robinson, Caleb Ortiz, Anthony Kim, Allen Dodhia, Rahul Zolli, Andrew Nagaraju, Shivaprakash K Oakleaf, James Kiesecker, Joe Ferres, Juan M. Lavista Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.14860 |