Training Samples Dataset of Building Identification in Urban Village
LIU Yufei1,2LV Beiru1,3PENG Ling1WU Tong1,3LIU Sai4
1 Aerospace Information Research Institute，Chinese Academy of Sciences，Beijing 100101，China2 Ucastech（Beijing）Smart Co. Ltd.，Beijing 100080，China3 University of Chinese Academy of Sciences，Beijing 100049，China4 Beijing Qingruanhaixin Technology Co. Ltd.，Beijing 100085，China
DOI:10.3974/geodb.2020.02.16.V1
Published:Apr. 2020
Visitors：12187 Data Files Downloaded：360
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Key Words：
urban village,building cluster,Deep learning,Mask R-CNN,Proceedings of the first China Digital Earth Conference
Abstract：
Buildings identifying from the remote sensing images is the important basic methodology for the urban management. The distribution pattern of the building clusters, especially the high building density, narrow streets, etc., are more critical for the urban management. Based on the remote sensing images of Google map, 2,328 samples of the building clusters in the urban village were drawn by using Labelme software. The building information was extracted by using Mask R-CNN, which is an example segmentation algorithm of deep learning. The dataset includes: (1) original sample images (Buildingsample_pic); (2) Sample segmentation result (Buildingsample_mask); (3) Sample segmentation annotation (Buildingsample_info). The dataset is consisted of 6,984 data files in three data folders, with .png and .yaml data formats. The data size is 499 MB (compressed into one file, 498 MB). The research paper related to the dataset will be published in the Proceedings of the first China Digital Earth Conference.Browse
Foundation Item：
The Beijing Municipal Science & Technology Project (Z191100001419002)
Data Citation：
LIU Yufei, LV Beiru, PENG Ling, WU Tong, LIU Sai. Training Samples Dataset of Building Identification in Urban Village[J/DB/OL]. Digital Journal of Global Change Data Repository, 2020. https://doi.org/10.3974/geodb.2020.02.16.V1.
LIU Yufei, LV Beiru, PENG Ling, et al. Training samples dataset of building identification in urban village [J]. Journal of Global Change Data & Discovery, 2020, 4(2): 181-187. DOI: 10.3974/geodp.2020.02.11.
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| 0 | Datapaper_Samples_BuiUrbanVill.pdf | 273.00kb | DownLoad |
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Samples_BuiUrbanVill.rar |
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