中文版 | English

在结果中检索

GSTDTAP

浏览/检索结果: 共7条，第1-7条 帮助

限定条件                            
已选(0)清除 条数/页：   排序方式：
Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices 期刊论文
LANDSCAPE AND URBAN PLANNING, 2019, 191
作者:  Ye, Yu;  Richards, Daniel;  Lu, Yi;  Song, Xiaoping;  Zhuang, Yu;  Zeng, Wei;  Zhong, Teng
收藏  |  浏览/下载：8/0  |  提交时间：2019/11/27
Visible greenery  Google Street View  Space syntax  Human-scale  Accessible greenery  Machine learning  
Dissecting racial bias in an algorithm used to manage the health of populations 期刊论文
SCIENCE, 2019, 366 (6464) : 447-+
作者:  Obermeyer, Ziad;  Powers, Brian;  Vogeli, Christine;  Mullainathan, Sendhil
收藏  |  浏览/下载：4/0  |  提交时间：2019/11/27
Machine-based understanding of manually collected visual and auditory datasets for urban perception studies 期刊论文
LANDSCAPE AND URBAN PLANNING, 2019, 190
作者:  Verma, Deepank;  Jana, Arnab;  Ramamritham, Krithi
收藏  |  浏览/下载：0/0  |  提交时间：2019/11/27
When robots sleep, do they dream of algorithms? 期刊论文
SCIENCE, 2019, 365 (6459) : 1330-1332
作者:  Dove, Alan
收藏  |  浏览/下载：0/0  |  提交时间：2019/11/27
On the Value of ENSO State for Urban Water Supply System Operators: Opportunities, Trade-Offs, and Challenges 期刊论文
WATER RESOURCES RESEARCH, 2019, 55 (4) : 2856-2875
作者:  Libisch-Lehner, C. P.;  Nguyen, H. T. T.;  Taormina, R.;  Nachtnebel, H. P.;  Galelli, S.
收藏  |  浏览/下载：1/0  |  提交时间：2019/11/26
Water Resources Assessment of China's Transboundary River Basins Using a Machine Learning Approach 期刊论文
WATER RESOURCES RESEARCH, 2019, 55 (1) : 632-655
作者:  Yan, Jiabao;  Jia, Shaofeng;  Lv, Aifeng;  Zhu, Wenbin
收藏  |  浏览/下载：13/0  |  提交时间：2019/04/09
water resources  runoff coefficient  machine learning  transboundary river  China  
Explaining subjective perceptions of public spaces as a function of the built environment: A massive data approach 期刊论文
LANDSCAPE AND URBAN PLANNING, 2019, 181: 169-178
作者:  Rossetti, Tomas;  Lobel, Hans;  Rocco, Victor;  Hurtubia, Ricardo
收藏  |  浏览/下载：1/0  |  提交时间：2019/04/09
Perceptions  Discrete choice models  Machine learning  Public spaces