A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data

• URL: http://arxiv.org/abs/2401.16437v1
• Date: Fri, 26 Jan 2024 21:47:39 GMT
• Title: A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data
• Authors: Mark S. Veillette, James M. Kurdzo, Phillip M. Stepanian, John Y. N. Cho, Siddharth Samsi and Joseph McDonald
• Abstract summary: This study introduces a new benchmark dataset, TorNet, to support development of Machine Learning algorithms in tornado detection and prediction. A novel deep learning (DL) architecture capable of processing raw radar imagery without the need for manual feature extraction is studied. Despite not benefiting from manual feature engineering or other preprocessing, the DL model shows increased detection performance compared to non-DL and operational baselines.
• Score: 4.1241397159763835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weather radar is the primary tool used by forecasters to detect and warn for tornadoes in near-real time. In order to assist forecasters in warning the public, several algorithms have been developed to automatically detect tornadic signatures in weather radar observations. Recently, Machine Learning (ML) algorithms, which learn directly from large amounts of labeled data, have been shown to be highly effective for this purpose. Since tornadoes are extremely rare events within the corpus of all available radar observations, the selection and design of training datasets for ML applications is critical for the performance, robustness, and ultimate acceptance of ML algorithms. This study introduces a new benchmark dataset, TorNet to support development of ML algorithms in tornado detection and prediction. TorNet contains full-resolution, polarimetric, Level-II WSR-88D data sampled from 10 years of reported storm events. A number of ML baselines for tornado detection are developed and compared, including a novel deep learning (DL) architecture capable of processing raw radar imagery without the need for manual feature extraction required for existing ML algorithms. Despite not benefiting from manual feature engineering or other preprocessing, the DL model shows increased detection performance compared to non-DL and operational baselines. The TorNet dataset, as well as source code and model weights of the DL baseline trained in this work, are made freely available.

Related papers

• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar [0.7279730418361995]
  High-resolution imaging 4D (3+1D) radar sensors have deep learning-based radar perception research. We investigate deep learning-based models operating on radar point clouds for 3D object detection.
  arXiv  Detail & Related papers  (2023-08-10T10:10:43Z)
• Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks [0.0]
  This work proposes a faster method for FMCW radar simulation capable of generating synthetic raw radar data using generative adversarial networks (GAN) The code and pre-trained weights are open-source and available on GitHub. Results have shown that the data is realistic in terms of coherent radar reflections of the motorcycle and background noise based on the comparison of chirps, the RA maps and the object detection results.
  arXiv  Detail & Related papers  (2023-08-04T17:44:27Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information [18.462990836437626]
  Automotive radar has increasingly attracted attention due to growing interest in autonomous driving technologies. We present a novel adaptive radar sub-sampling algorithm designed to identify regions that require more detailed/accurate reconstruction based on prior environmental conditions' knowledge.
  arXiv  Detail & Related papers  (2023-02-21T05:32:28Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification [0.5669790037378094]
  We propose a method that combines classical radar signal processing and Deep Learning algorithms. The proposed method can be used for example to improve automatic emergency braking or collision avoidance systems.
  arXiv  Detail & Related papers  (2022-02-17T08:45:11Z)
• Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
  The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. We propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
  arXiv  Detail & Related papers  (2021-11-17T21:48:31Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data [81.06749792332641]
  We propose a person detection network which uses an alternative strategy to combine scans obtained at different times. DR-SPAAM keeps the intermediate features from the backbone network as a template and recurrently updates the template when a new scan becomes available. On the DROW dataset, our method outperforms the existing state-of-the-art, while being approximately four times faster.
  arXiv  Detail & Related papers  (2020-04-29T11:01:44Z)
• Probabilistic Oriented Object Detection in Automotive Radar [8.281391209717103]
  We propose a deep-learning based algorithm for radar object detection. We created a new multimodal dataset with 102544 frames of raw radar and synchronized LiDAR data. Our best performing radar detection model achieves 77.28% AP under oriented IoU of 0.3.
  arXiv  Detail & Related papers  (2020-04-11T05:29:32Z)

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.