Abstract

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.

Measurement(s)

building rooftop area

Technology Type(s)

computational modeling technique

Sample Characteristic - Environment

city

Sample Characteristic - Location

China

Details

Title
Vectorized rooftop area data for 90 cities in China
Author
Zhang, Zhixin 1   VIAFID ORCID Logo  ; Qian Zhen 1   VIAFID ORCID Logo  ; Teng, Zhong 1 ; Chen, Min 2   VIAFID ORCID Logo  ; Zhang, Kai 1 ; Yang, Yue 1 ; Zhu, Rui 3 ; Zhang, Fan 4   VIAFID ORCID Logo  ; Zhang Haoran 5 ; Zhou Fangzhuo 1 ; Yu, Jianing 1 ; Zhang Bingyue 1 ; Lü Guonian 1 ; Yan Jinyue 6 

1  Nanjing Normal University, Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing, China (GRID:grid.260474.3) (ISNI:0000 0001 0089 5711); State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, China (GRID:grid.260474.3) (ISNI:0000 0001 0089 5711); Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China (GRID:grid.511454.0) 
2  Nanjing Normal University, Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing, China (GRID:grid.260474.3) (ISNI:0000 0001 0089 5711); State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, China (GRID:grid.260474.3) (ISNI:0000 0001 0089 5711); Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China (GRID:grid.511454.0); Jiangsu Provincial Key Laboratory for NSLSCS, School of Mathematical Science, Nanjing Normal University, Nanjing, China (GRID:grid.260474.3) (ISNI:0000 0001 0089 5711) 
3  The Hong Kong Polytechnic University, Department of Land Surveying and Geo-Informatics, Kowloon, China (GRID:grid.16890.36) (ISNI:0000 0004 1764 6123) 
4  Senseable City Lab, Massachusetts Institute of Technology, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
5  The University of Tokyo, Center for Spatial Information Science, Chiba, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X); LocationMind Inc, Tokyo, Japan (GRID:grid.26999.3d); Malardalen University, Future Energy Center, Vasteras, Sweden (GRID:grid.411579.f) (ISNI:0000 0000 9689 909X) 
6  Malardalen University, Future Energy Center, Vasteras, Sweden (GRID:grid.411579.f) (ISNI:0000 0000 9689 909X); KTH Royal Institute of Technology, Department of Chemical Engineering, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41597-022-01168-x
ProQuest document ID
2635112084
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.