Deep Earth Query: Information Discovery from Big Earth Observation Data Archives
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Earth observation (EO) data archives are explosively growing as a result of advances in satellite systems. As an example, remote sensing (RS) images acquired by ESA’s Sentinel satellites (which are a part of EU’s Copernicus program) reach the scale of more than 10 TB per day. The “big EO data” is a great source for information discovery and extraction for monitoring Earth from above. Thus, accurate and scalable techniques for RS image understanding, search and retrieval have recently emerged. In this talk, a general overview on scientific and practical problems related to RS image characterization, indexing and search from massive archives will be initially discussed. Then, our recent developments that can overcome these problems will be presented. Particular attention will be given to our deep hashing network that learns a semantic-based metric space, while simultaneously producing binary hash codes for scalable and accurate content-based indexing and retrieval of RS images. Finally, the BigEarthNet benchmark archive, which is one of the largest benchmark archives to support the deep learning studies in EO, will be introduced.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.