Tropical and Extratropical Cyclone Detection Using Deep Learning

• URL: http://arxiv.org/abs/2005.09056v1
• Date: Mon, 18 May 2020 20:09:20 GMT
• Title: Tropical and Extratropical Cyclone Detection Using Deep Learning
• Authors: Christina Kumler-Bonfanti, Jebb Stewart, David Hall, Mark Govett
• Abstract summary: Deep learning segmentation image models using the U-Net structure can identify areas missed by more restrictive approaches. Four U-Net models are designed for detection of tropical and extratropical cyclone Regions Of Interest. The extratropical cyclone U-Net model performed 3 times faster than the comparable model used to detect the same ROI.
• Score: 0.7025418443146436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine learning methods can help improve both speed and accuracy of this process. Specifically, deep learning image segmentation models using the U-Net structure perform faster and can identify areas missed by more restrictive approaches, such as expert hand-labeling and a priori heuristic methods. This paper discusses four different state-of-the-art U-Net models designed for detection of tropical and extratropical cyclone Regions Of Interest (ROI) from two separate input sources: total precipitable water output from the Global Forecasting System (GFS) model and water vapor radiance images from the Geostationary Operational Environmental Satellite (GOES). These models are referred to as IBTrACS-GFS, Heuristic-GFS, IBTrACS-GOES, and Heuristic-GOES. All four U-Nets are fast information extraction tools and perform with a ROI detection accuracy ranging from 80% to 99%. These are additionally evaluated with the Dice and Tversky Intersection over Union (IoU) metrics, having Dice coefficient scores ranging from 0.51 to 0.76 and Tversky coefficients ranging from 0.56 to 0.74. The extratropical cyclone U-Net model performed 3 times faster than the comparable heuristic model used to detect the same ROI. The U-Nets were specifically selected for their capabilities in detecting cyclone ROI beyond the scope of the training labels. These machine learning models identified more ambiguous and active ROI missed by the heuristic model and hand-labeling methods commonly used in generating real-time weather alerts, having a potentially direct impact on public safety.

Related papers

• Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques [0.20971479389679332]
  We propose a state-of-the-art (SOTA) deep learning sequential model, leveraging the Temporal Convolutional Network (TCN) To evaluate the performance of our proposed model, we conducted a comparative analysis with other popular models including Support Vector Regression (SVR), Long Short Term Memory (LSTM), and Transformer. The average metrics revealed that TCN outperformed the other models, with an average MAE of 0.011, RMSE of 0.023, $R2$ of 0.991, KGE of 0.992, and NSE of 0.991.
  arXiv  Detail & Related papers  (2024-04-20T09:02:50Z)
• Global versus Local: Evaluating AlexNet Architectures for Tropical Cyclone Intensity Estimation [0.0]
  We introduce two ensemble-based models based on AlexNet architecture to estimate tropical cyclone intensity. We evaluate the performance of both models against a deep learning benchmark model called textitDeepti using a publicly available cyclone image dataset.
  arXiv  Detail & Related papers  (2024-04-11T00:02:57Z)
• AirShot: Efficient Few-Shot Detection for Autonomous Exploration [18.09990606406497]
  Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples. Existing methods still face performance and efficiency challenges, mainly due to unreliable features and exhaustive class loops. We propose a new paradigm AirShot, and discover that, by fully exploiting the valuable correlation map, AirShot can result in a more robust few-shot object detection system.
  arXiv  Detail & Related papers  (2024-04-07T20:39:31Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data [0.0]
  We propose a pipeline for the automatic detection of ICMEs using a method that has recently proven successful in medical image segmentation. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56 minutes, and the end time with a MAE of 3 hours and 20 minutes.
  arXiv  Detail & Related papers  (2022-05-07T07:25:05Z)
• Channel Pruned YOLOv5-based Deep Learning Approach for Rapid and Accurate Outdoor Obstacles Detection [6.703770367794502]
  One-stage algorithm have been widely used in target detection systems that need to be trained with massive data. Due to their convolutional structure, they need more computing power and greater memory consumption. We apply pruning strategy to target detection networks to reduce the number of parameters and the size of model.
  arXiv  Detail & Related papers  (2022-04-27T21:06:04Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)

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.