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Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

arXiv 2302.09990 ↗First posted 20 Feb 2023cs.CVcited by 0

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Abstract (arXiv)

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this paper, recent relevant scientific investigation and practical efforts using Deep Learning (DL) models for weather radar data analysis and pattern recognition have been reviewed; particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization. In addition to recent achievements, the latest advancements and existing challenges are also presented and discussed in this paper, trying to lead to reasonable potentials and trends in this highly-concerned field.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 46 references that are in the map. First posted on arXiv 20 Feb 2023.

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12 of 46 references.

References · 12

  1. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  2. 2018Generative Image Inpainting with Contextual Attentioncited by 580
  3. 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 311
  4. 2017Deep Learning for Precipitation Nowcasting: A Benchmark and A New Modelcited by 182
  5. 2019Pluralistic Image Completioncited by 162
  6. 2018PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learningcited by 122
  7. 2018Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamicscited by 92
  8. 2016High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesiscited by 90
  9. 2018Shift-Net: Image Inpainting via Deep Feature Rearrangementcited by 87
  10. 2020SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecturecited by 71
  11. 2021Broad-UNet: Multi-scale feature learning for nowcasting taskscited by 25
  12. 2020TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfallcited by 19

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