An Artificial Intelligence Dataset for Solar Energy Locations in India
Microsoft AI for Good Research Lab（微软AI造福社会研究实验室）；Forum for the Future（未来论坛）；The Nature of Conservancy (TNC)（大自然保护协会（TNC））
Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker, Shivaprakash K Nagaraju, Caleb Robinson, Priyal Bhatia, Aditi Khurana, Jane Wang, Felipe Ov… 展开作者
Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker, Shivaprakash K Nagaraju, Caleb Robinson, Priyal Bhatia, Aditi Khurana, Jane Wang, Felipe Oviedo, Juan Lavista Ferres
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Accepted for publication in Nature Scientific Data
Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental values is high. To expedite development of solar energy, land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. In this work, we developed a spatially explicit machine learning model to map utility-scale solar projects across India using freely available satellite imagery with a mean accuracy of 92%. Our model predictions were validated by human experts to obtain a dataset of 1363 solar PV farms. Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure. Our analysis indicates that over 74% of solar development In India was built on landcover types that have natural ecosystem preservation, or agricultural value.
Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020)
TriRhenaTech联盟展示了计划于2020年5月13日在卡尔斯鲁厄举行的取消的三国“上莱茵人工智能研讨会”的论文集。TriRhenaTech联盟是上莱茵河三国大都市区的大学网络，包括位于富特旺根、凯泽斯鲁滕、卡尔斯鲁厄和奥芬堡的德国应用科学大学，巴登-符腾堡州合作州立大学Loerrach，法国大学网络阿尔萨斯理工大学，瑞士国立大学（由14所工程、建筑和管理领域的“高等院校”组成）和瑞士西北应用科学与艺术大学（University of Applied Sciences and Arts Northwestern Switzerland）。该联盟的共同目标是加强知识、研究和技术的转移，以及学生的跨境流动。
Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India
Berkeley Education Alliance for Research in Singapore（伯克利教育研究联盟（新加坡））；University of California, Berkeley（加州大学伯克利分校）；National Institute of Technology, Sikkim（锡金国立理工学院）；Universidad Castilla-La Mancha（卡斯蒂利亚-拉曼查大学）；CUNEF Universidad（库内夫大学）
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models
Nanjing University of Information Science and Technology（南京信息工程大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Hongkong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）
Design of a dynamic and self adapting system, supported with artificial intelligence, machine learning and real time intelligence for predictive cyber risk analytics in extreme environments, cyber risk in the colonisation of Mars
University of Oxford（牛津大学）；Cisco Research Centre（思科研究中心）；Cardiff University（卡迪夫大学）；University of Warwick（华威大学）
Artificial intelligence for artificial materials: moiré atom
The NSF AI Institute for Artificial Intelligence and Fundamental Interactions（美国国家科学基金会人工智能与基本相互作用人工智能研究所）；Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）
Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021
TriRhenaTech联盟展示了2021年10月27日在德国凯泽斯滕举行的“上莱茵人工智能研讨会”的已接受论文。会议的主题是人工智能在生命科学，智能系统，工业4.0，移动等领域的应用。TriRhenaTech联盟是上莱茵河三国大都市区的大学网络，包括位于富特旺根、凯泽斯鲁滕、卡尔斯鲁厄、奥芬堡和特里尔的德国应用科学大学，巴登-符腾堡州合作州立大学Loerrach，法国大学网络阿尔萨斯理工大学。瑞士国立大学（由14所工程、建筑和管理领域的“高等院校”组成）和瑞士西北应用科学与艺术大学（University of Applied Sciences and Arts Northwestern Switzerland）。该联盟的共同目标是加强知识、研究和技术的转移，以及学生的跨境流动。
