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浏览/检索结果: 共6条，第1-6条 帮助 限定条件 专题：武汉大学    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 A graph convolutional neural network for classification of building patterns using spatial vector data 期刊论文 ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2019, 卷号: 150 作者:  Yan, Xiongfeng;  Ai, Tinghua;  Yang, Min;  Yin, Hongmei 收藏  |  浏览/下载：4/0  |  提交时间：2019/12/05 Building pattern classification  Graph convolutional neural network  Machine learning  Spatial vector data  Graph Fourier transform  Deep learning   A formal model of neighborhood representation and applications in urban building aggregation supported by Delaunay triangulation 期刊论文 PLOS ONE, 2019, 卷号: 14, 期号: 7 作者:  Ai, Tinghua;  Yin, Hongmei;  Shen, Yilang;  Yang, Min;  Wang, Lu 收藏  |  浏览/下载：6/0  |  提交时间：2019/12/06 A graph convolutional neural network for classification of building patterns using spatial vector data 期刊论文 ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2019, 卷号: 150 作者:   收藏  |  浏览/下载：8/0  |  提交时间：2019/12/05 Building pattern classification  Graph convolutional neural network  Machine learning  Spatial vector data  Graph Fourier transform  Deep learning   A formal model of neighborhood representation and applications in urban building aggregation supported by Delaunay triangulation 期刊论文 PLOS ONE, 2019, 卷号: 14, 期号: 7 作者:  Ai, Tinghua;  Yin, Hongmei;  Shen, Yilang;  Yang, Min;  Wang, Lu 收藏  |  浏览/下载：8/0  |  提交时间：2019/12/05 A graph convolutional neural network for classification of building patterns using spatial vector data (Open Access) 期刊论文 ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 卷号: 150 作者:  Yan, Xiongfeng;  Ai, Tinghua;  Yang, Min;  Yin, Hongmei 收藏  |  浏览/下载：2/0  |  提交时间：2019/12/05 Enteral nutrition feeding in Chinese intensive care units: a cross-sectional study involving 116 hospitals 期刊论文 CRITICAL CARE, 2018, 卷号: 22 作者:  Xing, Juan;  Zhang, Zhongheng;  Ke, Lu;  Zhou, Jing;  Qin, Bingyu 收藏  |  浏览/下载：67/0  |  提交时间：2019/12/05 Enteral feeding  Intensive care units  Cross-sectional study