Roofpedia – Mapping Roofscapes with AI

Explore Sustainable Roofscapes Around the World

Roofpedia is an open registry of sustainable roofscapes around the world. It uses deep convolutional neural network to detect sustainable roof typologies from satellite images. Footprints of buildings identified with solar panels or rooftop greenery are tagged automatically, and the results are visualized above.

Roofpedia in a nutshell.
Roofpedia in a nutshell.

The Roofpedia Index

The Sustainable Roof Index by Roofpedia is a measure of the penetration of sustainable roof typologies in major cities around the world. Solar roofs and Green roofs mapped by Roofpedia are compared against the total number of buildings and areas of the buildings in a city. Aggregate scores are calculated for both solar and green coverage and cities are ranked with an combined score.

The results are given in the table below. You may click on cities to visit them on map.

RankCityBuildingsSolar Roofs%SR Count%SR AreaSolar ScoreGreen Roofs%GR Count%GR AreaGreen ScoreOverall Score
3Las Vegas203898053.917.3931920.97.8951
6New York343856772.09.44619245.617.22837
9San Diego283032370.87.4273731.311.01420
10Los Angeles509783840.86.3224190.84.7614
11San Jose1823147320.44.91426501.510.21313
14San Francisco1658145600.33.9103890.22.626

Disclaimer: Vancouver is green! Just not roof green compared to the top cities in the list.

More cities are getting added to Roofpedia as their satellite data become available. If you’d like to contribute satellite images to expand Roofpedia, please email Abraham Noah Wu, the lead developer.

Data and code

Full data and code of Roofpedia can be accessed at its GitHub repo.

Paper and attribution

A preprint of the paper describing the project can be found on arXiv.

If you use Roofpedia in a scientific context, please cite the paper:

  title={Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability}, 
  author={Abraham Noah Wu and Filip Biljecki},


Research and development

Abraham Noah Wu

Principal investigator

Filip Biljecki

Urban Analytics Lab, National University of Singapore (NUS)


This research is part of the project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the Start-Up Grant R-295-000-171-133.

We gratefully acknowledge the sources of the used input data. For more information, please see the aforementioned paper.

Abraham Noah Wu
Abraham Noah Wu
Research Assistant
Filip Biljecki
Filip Biljecki
Principal Investigator