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Vectorized rooftop area data for 90 cities in China
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
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2022 (English)In: Scientific Data, E-ISSN 2052-4463, Vol. 9, no 1, article id 66Article in journal (Refereed) Published
Abstract [en]

Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km2 across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively. 

Place, publisher, year, edition, pages
Nature Research , 2022. Vol. 9, no 1, article id 66
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-57625DOI: 10.1038/s41597-022-01168-xISI: 000763431100003Scopus ID: 2-s2.0-85125612675OAI: oai:DiVA.org:mdh-57625DiVA, id: diva2:1645035
Available from: 2022-03-16 Created: 2022-03-16 Last updated: 2022-06-07Bibliographically approved

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Zhang, HaoranYan, Jinyue

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Output format
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