报告人：程涛 英国伦敦大学学院 教授
时间：2023年5月12日（周五），14:00-16:00
地点：综合楼二楼国际会议厅
Tao Cheng (tao.cheng@ucl.ac.uk)，SpaceTimeLab
Department of Civil, Environmental and Geomatic Engineering, University College London
报告摘要
当前时空智能(SpaceTimeAI)和地理空间智能(GeoAI)已是热门的话题, 该研究领域旨在将计算机科学的最新方法(如深度学习)应用于地理空间问题。虽然深度学习方法因其对栅格数据的自然适用性而在图像处理中取得了巨大成功, 但仍未广泛应用于其他空间和时空数据类型。本演讲提出了使用基于网络(和图)的框架作为一般空间结构来表示通常由点、折线和多边形表示的时空过程的命题。我们举例说明了网络和基于图的SpaceTimeAI，从基于图的深度学习预测，到时空聚类和优化。这些应用展示了基于网络(图)的SpaceTimeAI在智慧城市应用中的优势，并介绍其在交通出行、警务和公共卫生等领域的应用。
Abstract
SpaceTimeAI and GeoAI are currently hot topics, applying the latest algorithms in computer science, such as deep learning, to spatiotemporal data. Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing, their applications in other spatial and space-time data types are still immature. This talk sets up the proposition of using a network (& graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points, polylines, and polygons. We illustrate network and graph-based SpaceTimeAI, from graph-based deep learning for prediction, to space-time clustering and optimisation. These applications demonstrate the advantages of network (graph)-based SpaceTimeAI for smart cities applications including transport & mobility, crime & policing, and public health.
Reference: http://jggs.chinasmp.com/EN/10.11947/j.JGGS.2022.0309
个人简介及照片：
程涛教授是伦敦大学学院地理信息学教授，图灵研究所研究员，大数据分析SpaceTimeLab (www.ucl.ac.uk/spacetimelab)的创始人和主任。这是一个多学科研究中心，旨在从政府、商业和社会的地理位置和时间戳的数据中获得可操作的见解和远见。她的研究兴趣包括人工智能和大数据、网络复杂性、城市分析（建模、预测、聚类、可视化和模拟），及其在交通、商业、健康、社交以及犯罪和自然灾害预防等方面的应用。她在英国和欧盟获得了2500多万英镑的研究经费，与英国的多个政府机构和企业有深度合作，包括伦敦交通局(TfL)，伦敦大警察局(London Metropolitan Police) ，英格兰公共卫生部(Public Health England) , 奥雅纳全球公司(ARUP)等。她发表了280多篇研究论文，并获得了众多国际最佳论文奖。
Biography
Tao Cheng (HDR, PhD, FICE, CEng) is a Professor in GeoInformatics, Fellow of Turing Institute, the Founder and Director of SpaceTimeLab for Big Data Analytics (www.ucl.ac.uk/spacetimelab) at University College London, a multi-disciplinary research centre that aims to gain actionable insights and foresights from geo-located and time-stamped data for government, business and society. Her research interests span AI and Big Data, network complexity, urban analytics (modelling, prediction, clustering, visualisation and simulation) with applications in transport and mobility, safety and security, business intelligence, and natural hazards prevention. She has secured more than £25M research grants in the UK and EU, working with government and industrial partners in the UK including Transport for London, the London Metropolitan Police Service, Public Health England and Arup, to name a few. She has published over 280 research articles and received numerous international best paper awards.
https://iris.ucl.ac.uk/iris/browse/profile?upi=TCHEN23
航海学院
国际合作与交流处
2023年5月10日
