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

• URL: http://arxiv.org/abs/2306.04738v1
• Date: Wed, 7 Jun 2023 19:20:01 GMT
• Title: MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challenge
• Authors: Miriam Cha, Gregory Angelides, Mark Hamilton, Andy Soszynski, Brandon Swenson, Nathaniel Maidel, Phillip Isola, Taylor Perron, Bill Freeman
• Abstract summary: MultiEarth 2023 is the second annual CVPR workshop aimed at the monitoring and analysis of the health of Earth ecosystems. This paper presents the challenge guidelines, datasets, and evaluation metrics.
• Score: 17.549467886161857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Towards A Comprehensive Assessment of AI's Environmental Impact [0.5982922468400899]
  Recent surge of interest in machine learning has sparked a trend towards large-scale adoption of AI/ML. There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle. This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations.
  arXiv  Detail & Related papers  (2024-05-22T21:19:35Z)
• Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond [101.15395503285804]
  General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI) In this survey, we embark on a comprehensive exploration of the latest advancements in world models. We examine challenges and limitations of world models, and discuss their potential future directions.
  arXiv  Detail & Related papers  (2024-05-06T14:37:07Z)
• FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models [24.141443217910986]
  We present the first unified Forest Monitoring Benchmark (FoMo-Bench) FoMo-Bench consists of 15 diverse datasets encompassing satellite, aerial, and inventory data. To further enhance the diversity of tasks and geographies represented in FoMo-Bench, we introduce a novel global dataset, TalloS.
  arXiv  Detail & Related papers  (2023-12-15T09:49:21Z)
• OpenForest: A data catalogue for machine learning in forest monitoring [22.94664416295411]
  Advancing forest monitoring offers advantages in mitigating human impacts and enhancing our comprehension of forest composition. We provide a comprehensive overview of 86 open access forest datasets across spatial scales. These datasets are grouped in OpenForest, a dynamic catalogue open to contributions that strives to reference all available open access forest datasets.
  arXiv  Detail & Related papers  (2023-11-01T03:59:20Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• ForestEyes Project: Conception, Enhancements, and Challenges [68.8204255655161]
  This work presents a Citizen Science project called ForestEyes. It uses volunteer's answers through the analysis and classification of remote sensing images to monitor deforestation regions in rainforests. To evaluate the quality of those answers, different campaigns/workflows were launched using remote sensing images from Brazilian Legal Amazon.
  arXiv  Detail & Related papers  (2022-08-24T17:48:12Z)
• MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation [10.918741492506502]
  This work proposes an adaptive real-time multimodal regression and generation framework for the MultiEarth Matrix Completion Challenge in CVPR 2022. It achieves superior performance on unseen test queries in this challenge with an LPIPS of 0.2226, a PSNR of 123.0372, and an SSIM of 0.6347.
  arXiv  Detail & Related papers  (2022-06-17T18:55:05Z)
• MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge [17.4371831579002]
  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.
  arXiv  Detail & Related papers  (2022-04-15T20:59:02Z)
• Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet [98.97255654573662]
  This work provides an overview of areas in which audio intelligence can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether.
  arXiv  Detail & Related papers  (2022-03-10T13:32:31Z)
• Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya [54.12023102155757]
  Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. We present a machine learning based approach to support ecological monitoring, with a focus on glaciers.
  arXiv  Detail & Related papers  (2020-12-09T12:48:06Z)
• The 1st Agriculture-Vision Challenge: Methods and Results [144.57794061346974]
  The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. This paper provides a summary of notable methods and results in the challenge.
  arXiv  Detail & Related papers  (2020-04-21T05:02:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.