# Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability

arXiv: https://arxiv.org/abs/2012.14349 · HTML: https://mlsanity.com/paper/2012.14349/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [Classification of Urban Morphology with Deep Learning: Application on Urban Vitality](https://mlsanity.com/paper/2105.09908/) (2021)
- [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025)
- [Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation](https://mlsanity.com/paper/2307.02574/) (2023)
- [BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics](https://mlsanity.com/paper/2406.08990/) (2024)
- [GANmapper: geographical data translation](https://mlsanity.com/paper/2108.04232/) (2021)
- [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://mlsanity.com/paper/2208.00881/) (2022)
- [Artificial Intelligence and Human Geography](https://mlsanity.com/paper/2312.08827/) (2023)
- [Beyond Binary Rooftop Mapping: A Four-Class Deep Learning Framework for Green Roof Potential Assessment from Open Swiss Geospatial Data](https://mlsanity.com/paper/2607.22342/) (2026)

## Abstract (arXiv)

Sustainable roofs, such as those with greenery and photovoltaic panels, contribute to the roadmap for reducing the carbon footprint of cities. However, research on sustainable urban roofscapes is rather focused on their potential and it is hindered by the scarcity of data, limiting our understanding of their current content, spatial distribution, and temporal evolution. To tackle this issue, we introduce Roofpedia, a set of three contributions: (i) automatic mapping of relevant urban roof typology from satellite imagery; (ii) an open roof registry mapping the spatial distribution and area of solar and green roofs of more than one million buildings across 17 cities; and (iii) the Roofpedia Index, a derivative of the registry, to benchmark the cities by the extent of sustainable roofscape in term of solar and green roof penetration. This project, partly inspired by its street greenery counterpart `Treepedia', is made possible by a multi-step pipeline that combines deep learning and geospatial techniques, demonstrating the feasibility of an automated methodology that generalises successfully across cities with an accuracy of detecting sustainable roofs of up to 100% in some cities. We offer our results as an interactive map and open dataset so that our work could aid researchers, local governments, and the public to uncover the pattern of sustainable rooftops across cities, track and monitor the current use of rooftops, complement studies on their potential, evaluate the effectiveness of existing incentives, verify the use of subsidies and fulfilment of climate pledges, estimate carbon offset capacities of cities, and ultimately support better policies and strategies to increase the adoption of instruments contributing to the sustainable development of cities.