学术报告

报告题目：Geographic Optimal Transport for Heterogeneous Data: Fusing Remote Sensing and Social Media

报 告 人：李军，教授、博导

报告时间：2021年1月29日（周五）下午15:00

报告形式：腾讯会议

会 议 ID：781403998（密码：123456）

主办单位：地下空间智能控制教育部工程研究中心

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报告摘要：

The fusion of heterogeneous remote sensing and social media data can fill the gaps in satellite image collections and improve the spatio-temporal resolution of the available datasets. As a result, it is being gradually adopted in multi-modal data analytics. Generally, the fusion of heterogeneous geographic data faces the following issues: 1) the probability density functions may differ from different data sources, and 2) the geo-locations may not be well aligned. The former one can be generally solved by performing an alignment of representations in the source and target domains using, for instance, domain adaptation. The latter issue is seldom considered in the fusion of heterogeneous geographic data. In this paper, we present a new method called geographic optimal transport (GOT) which aims at aligning representations and geolocations in simultaneous fashion. Experimental results demonstrate that the proposed GOT can accurately align spatially biased geo-referenced tweets to the flood phenomena, leading to the conclusion that GOT can effectively fuse heterogeneous remote sensing and social media data.

报告人简介：

李军，中山大学教授，博士生导师，IEEE Fellow，主要研究方向为高光谱遥感图像处理分析和应用，在地球科学与遥感领域期刊和会议上发表论文100多篇，Google学术引用7800多次，主要成果发表在PIEEE、RSE、IEEE TGRS等刊物上。现任遥感领域重要期刊IEEE JSTARS主编。