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

TL;DR

MultiEarth 2022 is a multimodal learning challenge focused on monitoring Amazon deforestation, providing datasets, guidelines, and benchmarks to compare various methods in real-world environmental monitoring tasks.

Contribution

It introduces a comprehensive benchmark with datasets and evaluation metrics for multimodal learning in deforestation analysis, fostering collaboration between environmental and machine learning communities.

Findings

Established a new benchmark for multimodal deforestation monitoring

Provided datasets and evaluation protocols for three sub-challenges

Facilitated comparison of multimodal learning methods in environmental science

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.