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
会 议 ID：781403998（密码：123456）
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.