报告人：Susie Xi Rao
报告人简介：Susie Xi Rao，苏黎世联邦理工学院（ETH）博士后研究员；卢塞恩应用科学与艺术大学（HSLU）讲师。她的研究兴趣包括图数据的模式挖掘、信息提取和自然语言处理，研究成果发表在AAAI、CIDR、CIKM、ICCHP、ICDE、ICJNLP、ICLR、JURIX、KDD、NAACL、VLDB和《中国经济周刊》等重要国际会议和期刊上。
报告摘要：In this talk, we discuss a first milestone in measuring the floorspace of buildings for 40 major Chinese cities. The intent is to maximize city coverage and, eventually provide longitudinal data. We use a multi-task object segmenter approach to learn the building footprint and height in the same framework in parallel: (1) we determine the surface area is covered by any buildings (the square footage of occupied land); (2) we determine floorspace from multi-image representations of buildings from various angles to determine the height of buildings. We use Sentinel-1 and -2 satellite images as our main data source. We provide a detailed description of our data, algorithms, and evaluations. In addition, we analyze the quality of reference data and their role for measuring the building floorspace with minimal error. We conduct extensive quantitative and qualitative analyses with Shenzhen as a case study using our multi-task learner. Finally, we conduct correlation studies between our results (on both pixel and aggregated urban area levels) and nightlight data to gauge the merits of our approach in studying urban development.