mlsanityData to 7 Oct 2026

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

arXiv 2306.04738 ↗First posted 7 Jun 2023cs.CV cs.AIcited by 6

Not on the map. Why?

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.

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

Builds on

3 of 28 references.

References · 3

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 231
  3. 2020SpaceNet 6: Multi-Sensor All Weather Mapping Datasetcited by 47

Built on by

Cited by year

Citing papers per year: 2023: 5; 2024: 0; 2025: 1.

6 papers.