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CDCEO'21 - First Workshop on Complex Data Challenges in Earth Observation

Published: 30 October 2021 Publication History

Abstract

High-resolution remote sensing technology for Earth Observation (EO) has radically changed how we monitor the state of our planet around the clock. An effective interpretation of the resulting complex large-scale time series adopts the best machine learning techniques from signal processing, computer vision, pattern recognition, and artificial intelligence. The First Workshop on Complex Data Challenges in Earth Observation was open to both method development and advanced applications in a wide range of related topics, including image and signal processing, gap-filling, data fusion, feature extraction, prediction of spatio-temporal features, and the detection of rules underlying the observed state transitions and causal relationships. The full agenda, featuring keynotes and a selection of high quality contributed talks is available online at www.iarai.ac.at/cdceo21

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  • (2024)A Novel Transformer Network With Shifted Window Cross-Attention for Spatiotemporal Weather ForecastingIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.332372917(45-55)Online publication date: 2024
  • (2022)Mapping Dwellings in IDP/Refugee Settlements Using Deep LearningRemote Sensing10.3390/rs1424638214:24(6382)Online publication date: 16-Dec-2022
  • (2021)High-resolution multi-channel weather forecasting – First insights on transfer learning from the Weather4cast Competitions 20212021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9672063(5750-5757)Online publication date: 15-Dec-2021
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cover image ACM Conferences
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
October 2021
4966 pages
ISBN:9781450384469
DOI:10.1145/3459637
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2021

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Author Tags

  1. earth observation
  2. machine learning
  3. remote sensing
  4. weather

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CIKM '21
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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2024)A Novel Transformer Network With Shifted Window Cross-Attention for Spatiotemporal Weather ForecastingIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.332372917(45-55)Online publication date: 2024
  • (2022)Mapping Dwellings in IDP/Refugee Settlements Using Deep LearningRemote Sensing10.3390/rs1424638214:24(6382)Online publication date: 16-Dec-2022
  • (2021)High-resolution multi-channel weather forecasting – First insights on transfer learning from the Weather4cast Competitions 20212021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9672063(5750-5757)Online publication date: 15-Dec-2021
  • (2021)UNet based future weather prediction on Weather4cast 2021 stage 22021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9672032(5747-5749)Online publication date: 15-Dec-2021
  • (2021)Improvements to short-term weather prediction with recurrent-convolutional networks2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671869(5764-5769)Online publication date: 15-Dec-2021
  • (2021)Spatiotemporal Vision Transformer for Short Time Weather Forecasting2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671442(5741-5746)Online publication date: 15-Dec-2021
  • (2021)Enhanced Variational U-Net for Weather Forecasting2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671346(5758-5763)Online publication date: 15-Dec-2021

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