# MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challenge

arXiv: https://arxiv.org/abs/2306.04738 · HTML: https://mlsanity.com/paper/2306.04738/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [SpaceNet 6: Multi-Sensor All Weather Mapping Dataset](https://mlsanity.com/paper/2004.06500/) (2020)

## Built on by (most cited first, 6 of 6)

- [TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series](https://mlsanity.com/paper/2505.08723/) (2025)
- [FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2](https://mlsanity.com/paper/2311.03339/) (2023)
- [MultiEarth 2023 Deforestation Challenge -- Team FOREVER](https://mlsanity.com/paper/2306.11762/) (2023)
- [Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery](https://mlsanity.com/paper/2307.04916/) (2023)
- [1st Place Solution to MultiEarth 2023 Challenge on Multimodal SAR-to-EO Image Translation](https://mlsanity.com/paper/2306.12626/) (2023)
- [CVPR MultiEarth 2023 Deforestation Estimation Challenge:SpaceVision4Amazon](https://mlsanity.com/paper/2307.04715/) (2023)

## Abstract (arXiv)

The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) is the second annual CVPR workshop aimed at the monitoring and analysis of the health of Earth ecosystems by leveraging the vast amount of remote sensing data that is continuously being collected. The primary objective of this workshop is to bring together the Earth and environmental science communities as well as the multimodal representation learning communities to explore new ways of harnessing technological advancements in support of environmental monitoring. The MultiEarth Workshop also seeks to provide a common benchmark for processing multimodal remote sensing information by organizing public challenges focused on monitoring the Amazon rainforest. These challenges include estimating deforestation, detecting forest fires, translating synthetic aperture radar (SAR) images to the visible domain, and projecting environmental trends. This paper presents the challenge guidelines, datasets, and evaluation metrics. Our challenge website is available at https://sites.google.com/view/rainforest-challenge/multiearth-2023.