MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge

• URL: http://arxiv.org/abs/2204.07649v1
• Date: Fri, 15 Apr 2022 20:59:02 GMT
• Title: MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge
• Authors: Miriam Cha, Kuan Wei Huang, Morgan Schmidt, Gregory Angelides, Mark Hamilton, Sam Goldberg, Armando Cabrera, Phillip Isola, Taylor Perron, Bill Freeman, Yen-Chen Lin, Brandon Swenson, Jean Piou
• Abstract summary: The goal of the Challenge is to provide a common benchmark for multimodal information processing. This paper presents the challenge guidelines, datasets, and evaluation metrics for the three sub-challenges.
• Score: 17.4371831579002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022) will be the first competition aimed at the monitoring and analysis of deforestation in the Amazon rainforest at any time and in any weather conditions. The goal of the Challenge is to provide a common benchmark for multimodal information processing and to bring together the earth and environmental science communities as well as multimodal representation learning communities to compare the relative merits of the various multimodal learning methods to deforestation estimation under well-defined and strictly comparable conditions. MultiEarth 2022 will have three sub-challenges: 1) matrix completion, 2) deforestation estimation, and 3) image-to-image translation. This paper presents the challenge guidelines, datasets, and evaluation metrics for the three sub-challenges. Our challenge website is available at https://sites.google.com/view/rainforest-challenge.

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