mlsanityData to 7 Oct 2026

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

arXiv 2503.14860 ↗First posted 19 Mar 2025cs.LG cs.CVcited by 2

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Abstract (arXiv)

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 r2 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 45 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

15 of 45 references.

References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  4. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  5. 2018Object Detection with Deep Learning: A Reviewcited by 524
  6. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  7. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 164
  8. 2020A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagerycited by 87
  9. 2018Where are the Blobs: Counting by Localization with Point Supervisioncited by 54
  10. 2019Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural networkcited by 33
  11. 2018Unsupervised Hard Example Mining from Videos for Improved Object Detectioncited by 30
  12. 2023Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado eventcited by 8
3 more
  1. 2016Bootstrapping Face Detection with Hard Negative Examplescited by 6
  2. 2021Mapping industrial poultry operations at scale with deep learning and aerial imagerycited by 6
  3. 2022An Artificial Intelligence Dataset for Solar Energy Locations in Indiacited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detectioncited by 0
  2. 2026Recoverable Geographic Location Information in Earth-Observation Embeddingscited by 0