Applications of machine learning to wind engineering
Advances of the analytical, numerical, experimental and field-measurement approaches in
wind engineering offers unprecedented volume of data that, together with rapidly evolving …
wind engineering offers unprecedented volume of data that, together with rapidly evolving …
[HTML][HTML] Machine learning for bridge wind engineering
Z Zhang, S Li, H Feng, X Zhou, N Xu, H Li… - Advances in Wind …, 2024 - Elsevier
Modeling and control are primary domains in bridge wind engineering. The natural wind
field characteristics (eg, non-stationary, non-uniform, spatial-temporal changing …
field characteristics (eg, non-stationary, non-uniform, spatial-temporal changing …
Torchgeo: deep learning with geospatial data
Remotely sensed geospatial data are critical for applications including precision agriculture,
urban planning, disaster monitoring and response, and climate change research, among …
urban planning, disaster monitoring and response, and climate change research, among …
Digital typhoon: Long-term satellite image dataset for the spatio-temporal modeling of tropical cyclones
This paper presents the official release of the Digital Typhoon dataset, the longest typhoon
satellite image dataset for 40+ years aimed at benchmarking machine learning models for …
satellite image dataset for 40+ years aimed at benchmarking machine learning models for …
Biogeosciences perspectives on integrated, coordinated, open, networked (ICON) science
This article is composed of three independent commentaries about the state of Integrated,
Coordinated, Open, Networked (ICON) principles in the American Geophysical Union …
Coordinated, Open, Networked (ICON) principles in the American Geophysical Union …
[HTML][HTML] Tropical cyclone intensity estimation using Himawari-8 satellite cloud products and deep learning
This study develops an objective deep-learning-based model for tropical cyclone (TC)
intensity estimation. The model's basic structure is a convolutional neural network (CNN) …
intensity estimation. The model's basic structure is a convolutional neural network (CNN) …
Advanced machine learning methods for major hurricane forecasting
Hurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the
worst natural disasters in the 21st century. Further studies integrating the changing …
worst natural disasters in the 21st century. Further studies integrating the changing …
High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee …
The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working
Group (WG) was recently established under the IEEE Geoscience and Remote Sensing …
Group (WG) was recently established under the IEEE Geoscience and Remote Sensing …
A neural network with spatiotemporal encoding module for tropical cyclone intensity estimation from infrared satellite image
Accurate and instant estimation of tropical cyclone (TC) intensity is crucial for emergency
decision making. Although deep neural networks and satellite images have been …
decision making. Although deep neural networks and satellite images have been …
An intelligent optimized cyclone intensity prediction framework using satellite images
CKK Reddy, PR Anisha, MM Hanafiah… - Earth Science …, 2023 - Springer
Weather prediction is the hottest topic in remote sensing to understand natural disasters and
their intensity in an early stage. But in many cases, the typical imaging models have resulted …
their intensity in an early stage. But in many cases, the typical imaging models have resulted …