Global Renewables Watch

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 a R2 values of 0.96 and 0.93 for solar PV and onshore wind respectively compared to IRENA's most recent 2023 country level capacity estimates.

Dataset Download

Download the complete dataset from the release page, or use direct links below:

• Solar PV Dataset (2024 Q2): download
• Wind Turbine Dataset (2024 Q2): download

The datasets are provided as GeoPackage (.gpkg) files containing global detections with construction dates and land use information.

Model Inference

We provide two inference scripts: inference_solar.py for solar panel detection and inference_wind.py for wind turbine detection with the pretrained models. These each take a single GeoTIFF image as input and output a prediction GeoTIFF of the same shape.

Setup

First, setup a conda environment using the provided environment.yml file:

conda env create -f environment.yml
conda activate grw

Then download the pre-trained models to the models/ directory:

mkdir -p models
wget -O models/solar_model.ckpt https://github.com/microsoft/global-renewables-watch/releases/download/v1.1/solar_model.ckpt
wget -O models/wind_model.pth https://github.com/microsoft/global-renewables-watch/releases/download/v1.1/wind_model.pth

Solar Panel Inference

Run the solar panel detection model on a single GeoTIFF image:

python inference_solar.py \
    --model-fn models/solar_model.ckpt \
    --input-fn data/example_image.tif \
    --output-dir results/ \
    --gpu 0 \
    --verbose

Wind Turbine Inference

Run the wind turbine detection model on a single GeoTIFF image:

python inference_wind.py \
    --model-fn models/wind_model.pth \
    --input-fn data/example_image.tif \
    --output-dir results/ \
    --gpu 0 \
    --verbose

Notes

• Solar inference expects 4096x4096 pixel images
• Wind inference can handle arbitrary image sizes using a sliding window approach
• Both scripts support skip/overwrite modes to avoid reprocessing existing outputs
• GPU is recommended for faster inference but not required
• Output files maintain the same geospatial reference as input files

Polygonization

After running inference, use polygonize.py to convert raster predictions into vector features (GeoJSON format). This script automatically processes both solar and wind predictions:

• Solar: Extracts polygon features of detected solar panels (filtered by minimum area)
• Wind: Extracts centroid points of detected wind turbines

Usage

python polygonize.py \
    --input-dir results/ \
    --output-dir vectors/ \
    --min-area 10000 \
    --num-workers 6 \
    --verbose

Output:

• Solar predictions → *_solar.geojson (Polygon features with area)
• Wind predictions → *_wind.geojson (Point features with area)
• All geometries are in EPSG:3857 projection
• Features include filename and area (in m²) properties

Citation

If you use this work, please consider citing our paper:

@article{robinson2025global,
  title={Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery},
  author={Robinson, Caleb and Ortiz, Anthony and Kim, Allen and Dodhia, Rahul and Zolli, Andrew and Nagaraju, Shivaprakash K and Oakleaf, James and Kiesecker, Joe and Ferres, Juan M Lavista},
  journal={arXiv preprint arXiv:2503.14860},
  year={2025}
}

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