報告人：程濤 英國倫敦大學學院 教授

時間：2023年5月12日（周五），14:00-16:00

地點：綜合樓二樓國際會議廳

Tao Cheng ([email protected])，SpaceTimeLab

Department of Civil, Environmental and Geomatic Engineering, University College London

報告摘要

當前時空智能(SpaceTimeAI)和地理空間智能(GeoAI)已是熱門的話題, 該研究領域旨在將計算機科學的最新方法(如深度學習)應用于地理空間問題。雖然深度學習方法因其對柵格數據的自然適用性而在圖像處理中取得了巨大成功, 但仍未廣泛應用于其他空間和時空數據類型。本演講提出了使用基于網絡(和圖)的框架作為一般空間結構來表示通常由點、折線和多邊形表示的時空過程的命題。我們舉例說明了網絡和基于圖的SpaceTimeAI，從基于圖的深度學習預測，到時空聚類和優化。這些應用展示了基于網絡(圖)的SpaceTimeAI在智慧城市應用中的優勢，并介紹其在交通出行、警務和公共衛生等領域的應用。

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

個人簡介及照片：

程濤教授是倫敦大學學院地理信息學教授，圖靈研究所研究員，大數據分析SpaceTimeLab (www.ucl.ac.uk/spacetimelab)的創始人和主任。這是一個多學科研究中心，旨在從政府、商業和社會的地理位置和時間戳的數據中獲得可操作的見解和遠見。她的研究興趣包括人工智能和大數據、網絡復雜性、城市分析（建模、預測、聚類、可視化和模擬），及其在交通、商業、健康、社交以及犯罪和自然災害預防等方面的應用。她在英國和歐盟獲得了2500多萬英鎊的研究經費，與英國的多個政府機構和企業有深度合作，包括倫敦交通局(TfL)，倫敦大警察局(London Metropolitan Police) ，英格蘭公共衛生部(Public Health England) , 奧雅納全球公司(ARUP)等。她發表了280多篇研究論文，并獲得了眾多國際最佳論文獎。

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

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