4.7 Article

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

期刊

LANDSCAPE AND URBAN PLANNING
卷 214, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.landurbplan.2021.104167

关键词

Sustainable development; Convolutional Neural Network; Computer vision; Carbon neutrality; Building data; OpenStreetMap

资金

  1. National University of Singapore [R-295000-171-133]

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Roofpedia project introduces a derivative index based on satellite imagery and data registry to assess the extent of sustainable roofscapes in cities, assisting researchers, local governments, and the public in understanding patterns of sustainable rooftop utilization, tracking current rooftop usage, evaluating the effectiveness of existing incentives, verifying subsidy usage and climate promises fulfillment, estimating carbon offset capacities, and ultimately supporting better policies and strategies to increase adoption of sustainable instruments for urban development.
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.

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